Supervised classification: Why is the label built-up area not showing in the graph but instead the number 8?

Once exporting my work the graph seems to show the number 8 instead of the label built up area. What is the problem?

This is my code:

var image = ee.Image(ee.ImageCollection('LANDSAT/LT05/C01/T1_SR')
    // Filter to get only images under the region of interest.
    .filterBounds(roi)
    // Filter to get only one year of images.
    .filterDate('1986-01-01', '1986-12-30')
    // Sort by scene cloudiness, ascending.
    .sort('CLOUD_COVER')
    // Get the first (least cloudy) scene.
    .first());

Map.addLayer(image, {bands: ['B5', 'B4', 'B3'], max: 0.5, gamma: 2}, "L8 Image");
// Merge points together
var newfc = Water.merge(naturalwoodlands).merge(plantedforest).merge(builtuparea).merge(cultivatedland).merge(grassland).merge(barrenland).merge(saltmarsh);
print(newfc, 'newfc');
// Select the bands to be used in training
var bands = ['B2', 'B3', 'B4', 'B5', 'B6', 'B7'];

// Sample the input imagery to get a FeatureCollection of training data.
var training = image.select(bands).sampleRegions({
  collection: newfc,
  properties: ['landuse'],
  scale: 30  // should reflect the scale of your imagery
});
// Make a classifier and train it.
var classifier = ee.Classifier.randomForest(10).train({
  features: training,
  classProperty: 'landuse',
  inputProperties: bands
});
// Classify the input imagery.
var classified = image.select(bands).classify(classifier).aside(print);

// Define a palette for the Land Use classification.
var palette = [
'#0a9917', // naturalwoodlands(0)
'#00f7f7', // water(1)
'#fffcb0', // cultivatedland(2)
'#d7d8ff', // builtuparea(3)
'#1aff0b', // plantedforest(4)
'#e766ff' , // grassland(5)
'#1612f7', // barrenland(6)
'#a054ff',// saltmarsh(7)
];
// Display th#ff4b38e classification result and the input image.
Map.setCenter(23.054, -34.0482);
Map.addLayer(classified, {min: 0, max: 6, palette: palette}, 'Land Cover Classification');
// Get a confusion matrix representing resubstitution accuracy.
var trainAccuracy = classifier.confusionMatrix();
print('Resubstitution error matrix: ', trainAccuracy);
print('Training overall accuracy: ', trainAccuracy.accuracy());

// var ghnp = ee.FeatureCollection('ft:1Kh5uxAdbnuF77vydlLPCAOQ-A7HgLxyYFyI4QnnT', 'geometry');
// Map.addLayer(ghnp, null, 'roi');

var areaChart = ui.Chart.image.byClass({
  image: ee.Image.pixelArea().divide(1000000).addBands(classified),
  classBand: 'classification', 
  region: geometry,
  scale: 30,
  reducer: ee.Reducer.sum(),
  classLabels: ['water', 'naturalwoodlands','plantedforest','builtuparea','cultivatedland','grassland','barrenland','saltmarsh']
}).setOptions({
  hAxis: {title: 'Classes'},
  vAxis: {title: 'Area sqkm'},
  title: 'A histogram showing area by class',
  series: {
    0: { color: 'blue' },
    1: { color: 'green' },
    2: { color: 'brown' },
    3: { color: 'gray' },
    4: { color: 'yellow' },
     5: { color: 'purple' },
      6: { color: 'pink' },
       7: { color: 'black' },
  }
});
print(areaChart);
var exportAccuracy = ee.Feature(null, {matrix: trainAccuracy.array()})

// Export the FeatureCollection.
Export.table.toDrive({
  collection: ee.FeatureCollection(exportAccuracy),
  description: 'exportAccuracy',
  fileFormat: 'CSV'
});
// Get a confusion matrix representing resubstitution accuracy.
print('RF error matrix: ', classifier.confusionMatrix());
print('RF accuracy: ', classifier.confusionMatrix().accuracy());
Export.image.toDrive({
 image: classified,
   description: 'LC_2018', 
   scale: 30,
   region: geometry,
maxPixels: 130000000000,
 });
var exportAccuracy = ee.Feature(null, {matrix: trainAccuracy.array()})

// Export the FeatureCollection.
Export.table.toDrive({
  collection: ee.FeatureCollection(exportAccuracy),
  description: 'exportAccuracy',
  fileFormat: 'CSV'
});