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: "Emotions and Information Diffusion in Social Media—Sentiment of Microblogs and Sharing Behavior"
  Abstract: As a new communication paradigm, social media has promoted information dissemination in social networks. Previous research has identified several content-related features as well as user and network characteristics that may drive information diffusion. However, little research has focused on the relationship between emotions and information diffusion in a social media setting. In this paper, we examine whether sentiment occurring in social media content is associated with a user's information sharing behavior. We carry out our research in the context of political communication on Twitter. Based on two data sets of more than 165,000 tweets in total, we find that emotionally charged Twitter messages tend to be retweeted more often and more quickly compared to neutral ones. As a practical implication, companies should pay more attention to the analysis of sentiment related to their brands and products in social media communication as well as in designing advertising content that triggers emotions.

Connected through hyperedge HE-1 (induced by source paper "SOCIAL COMMERCE: THE CRITICAL ROLE OF ARGUMENT STRENGTH AND SOURCE DYNAMISM OF EWOM"):
  with members:
    (none)

Connected through hyperedge HE-2 (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"
    - "Active Learning for Imbalanced Sentiment Classification"

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