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: "Extracting Knowledge from Web Search Engine Using Wikipedia"
  Abstract: Nowadays, search engines are definitely a dominating web tool for finding information on the web. However, web search engines usually return web page references in a global ranking making it difficult to the users to browse different topics captured in the result set. Recently, there are meta-search engine systems that discover knowledge in these web search results providing the user with the possibility to browse different topics contained in the result set. In this paper, we focus on the problem of determining different thematic groups on web search engine results that existing web search engines provide. We propose a novel system that exploits semantic entities of Wikipedia for grouping the result set in different topic groups, according to the various meanings of the provided query. The proposed method utilizes a number of semantic annotation techniques using Knowledge Bases, like WordNet and Wikipedia, in order to perceive the different senses of each query term. Finally, the method annotates the extracted topics using information derived from clusters which in following are presented to the end user.

Connected through hyperedge HE-1 (induced by source paper "Improved text annotation with Wikipedia entities"):
  with members:
    - "Named entity disambiguation by leveraging wikipedia semantic knowledge"
    - "Classification of short texts by deploying topical annotations"

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