human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Sentiment analysis of customer reviews: balanced versus unbalanced datasets"
  Abstract: More people are buying products online and expressing their opinions on these products through online reviews. Sentiment analysis can be used to extract valuable information from reviews, and the results can benefit both consumers and manufacturers. This research shows a study which compares two well known machine learning algorithms namely, dynamic language model and naive Bayes classifier. Experiments have been carried out to determine the consistency of results when the datasets are of different sizes and also the effect of a balanced or unbalanced dataset. The experimental results indicate that both the algorithms over a realistic unbalanced dataset can achieve better results than the balanced datasets commonly used in research.

Connected through hyperedge HE-1 (induced by source paper "Opinion observer: analyzing and comparing opinions on the Web"):
  with members:
    - "Twitter power: Tweets as electronic word of mouth"
    - "Opinion Mining and Sentiment Analysis"
    - "Utility scoring of product reviews"
    - "Mining newsgroups using networks arising from social behavior"
    - "Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning"
    - "Combining resources to improve unsupervised sentiment analysis at aspect-level"
    - "Understand the City Better: Multimodal Aspect-Opinion Summarization for Travel"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science