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: "Optimal Structure Weighted Retrieval."
  Abstract: Improving ranking functions for structured information retrieval has received much attention since the inception of XML. Weighting document structures is one method providing significant improvement – but how good can these improvements be? Optimal structure weighted retrieval occurs when each query is processed using the optimal set of weights for that query. Optimal retrieval for a set of queries occurs when a set of weights optimized for that set of queries is used. Measuring mean average precision for each of these will give a performance upper bound for document structure weighted retrieval. In this investigation a near optimal set of weights is learned for TREC WSJ collection topics 101-200 using a genetic algorithm. Weights are learned for vector space inner product, naive probability and BM25 ranking functions and a performance upper bound is calculated. The upper bound using a different set of weights for each query, gives mean average precision improvements of about 15% for BM25 and naive probability; about 30% for inner product. This suggests structure weighting might be useful for relevance feedback. Optimal weights for the set of queries shows improvements of about 5% for naive probability and inner product, but of only about 1% for BM25; suggesting this technique is not as effective for ad hoc retrieval.

Connected through hyperedge HE-1 (induced by source paper "Choosing document structure weights"):
  with members:
    - "XIRQL: a query language for information retrieval in XML documents"

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