input: Write a function to classify the sentiment of movie reviews using Natural Language Processing. The function should use the VADER sentiment analysis tool from the NLTK library. If the negative sentiment score is higher than the positive score, return 'negative'. Otherwise, return 'positive'. If the input sentence is empty, return 'neutral'.
  
  assert classify_sentiment("The movie was terrible. It felt like it would never end!") == 'negative'
  assert classify_sentiment("This film was absolutely fantastic!") == 'positive'
  assert classify_sentiment("") == 'neutral'
output: def classify_sentiment(review):
      from nltk.sentiment.vader import SentimentIntensityAnalyzer
      if not review:
          return 'neutral'
      analyzer = SentimentIntensityAnalyzer()
      scores = analyzer.polarity_scores(review)
      if scores['neg'] > scores['pos']:
          return 'negative'
      else:
          return 'positive'