input: Write a function to find the main topic of a sentence using POS tagging.
  assert find_main_topic('This sentence is related to NLP') == 'sentence'
  assert find_main_topic('The cat sat on the mat') == 'cat'
  assert find_main_topic('Artificial Intelligence and Machine Learning are popular fields') == 'Artificial'
output: def find_main_topic(sentence):
      import nltk
      from nltk.tokenize import word_tokenize
      from nltk.corpus import stopwords
      from nltk.tag import pos_tag
  
      nltk.download('punkt')
      nltk.download('averaged_perceptron_tagger')
      nltk.download('stopwords')
  
      # Tokenize the sentence
      tokens = word_tokenize(sentence)
      
      # Remove stop words
      stop_words = set(stopwords.words('english'))
      filtered_tokens = [word for word in tokens if word.lower() not in stop_words]
      
      # Perform POS tagging
      tagged_tokens = pos_tag(filtered_tokens)
      
      # Extract nouns and proper nouns
      nouns = [word for word, pos in tagged_tokens if pos in ('NN', 'NNP', 'NNS', 'NNPS')]
      
      # Return the first noun/proper noun as the main topic
      return nouns[0] if nouns else None