input: Write a function to classify the sentiment of movie reviews using Natural Language Processing and VADER sentiment analysis tool from nltk library. The function should take a sentence as input and return a string indicating whether the sentiment of the sentence is 'negative' or 'positive'. If the negative score is higher than the positive score, the sentiment is 'negative'. Otherwise, it is 'positive'.
  
  import nltk
  from nltk.sentiment import vader
  
  def classify_sentiment(sentence):
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(sentence)
      
      if scores['neg'] > scores['pos']:
          return 'negative'
      else:
          return 'positive'
  
  assert classify_sentiment('The movie was terrible. It felt like it would never end!') == 'negative'
  assert classify_sentiment('The movie was amazing! I loved every moment of it.') == 'positive'
output: def classify_sentiment(sentence):
      sid = vader.SentimentIntensityAnalyzer()
      scores = sid.polarity_scores(sentence)
      
      if scores['neg'] > scores['pos']:
          return 'negative'
      else:
          return 'positive'