input: Write a function to predict the class labels of a given set of data. The function should load a dataset from a specified URL, split it into features and label, train a DecisionTreeClassifier model on the data, and then predict the label for a new set of features. 
  
  from sklearn.tree import DecisionTreeClassifier
  import pandas as pd
  
  def predict_class(csv_url, data):
      df = pd.read_csv(csv_url)
      X = df.iloc[:, 0:4].values
      y = df.iloc[:, 4].values
      model = DecisionTreeClassifier()
      model.fit(X, y)
      predicted_label = model.predict([data])
      return predicted_label
  
  assert predict_class('http://test.test/test.csv', [1.0, 2.0, 3.0, 4.0])==['classA']
  assert predict_class('http://test.test/test.csv', [10.0, 20.0, 30.0, 40.0])==['classB']
  assert predict_class('http://test.test/test.csv', [5.0, 5.0, 5.0, 5.0])==['classC']
output: def predict_class(csv_url, data):
      df = pd.read_csv(csv_url)
      X = df.iloc[:, 0:4].values
      y = df.iloc[:, 4].values
      model = DecisionTreeClassifier()
      model.fit(X, y)
      predicted_label = model.predict([data])
      return predicted_label