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: "Expressive signals in social media languages to improve polarity detection"
  Abstract: To capture the sentiment of messages, several expressive forms are investigated.Expressive signals enrich the feature space of baseline and ensemble classifiers.Only adjectives play a fundamental role as expressive signal.Pragmatic particles and expressive lengthening could lead to the de finition of erratic polarity classifiers. Social media represents an emerging challenging sector where the natural language expressions of people can be easily reported through blogs and short text messages. This is rapidly creating unique contents of massive dimensions that need to be efficiently and effectively analyzed to create actionable knowledge for decision making processes. A key information that can be grasped from social environments relates to the polarity of text messages. To better capture the sentiment orientation of the messages, several valuable expressive forms could be taken into account. In this paper, three expressive signals - typically used in microblogs - have been explored: (1) adjectives, (2) emoticon, emphatic and onomatopoeic expressions and (3) expressive lengthening. Once a text message has been normalized to better conform social media posts to a canonical language, the considered expressive signals have been used to enrich the feature space and train several baseline and ensemble classifiers aimed at polarity classification. The experimental results show that adjectives are more discriminative and impacting than the other considered expressive signals.

Connected through hyperedge HE-1 (induced by source paper "Opinion Mining and Sentiment Analysis"):
  with members:
    - "Using Universal Linguistic Knowledge to Guide Grammar Induction"
    - "Opinion Mining and Information Fusion"
    - "Sentiment analysis in Facebook and its application to e-learning"
    - "Utility scoring of product reviews"
    - "Opinion observer: analyzing and comparing opinions on the Web"
    - "Mining newsgroups using networks arising from social behavior"
    - "User-level sentiment analysis incorporating social networks"
    - "Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior"

Connected through hyperedge HE-2 (induced by source paper "Extracting Diverse Sentiment Expressions With Target-dependent Polarity from Twitter"):
  with members:
    - "Opinion Mining and Sentiment Analysis"

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