human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge groups authors who co-authored the same paper.

Details of the hypergraph:
Center hyperedge (induced by source paper "Using Universal Linguistic Knowledge to Guide Grammar Induction"):
  Abstract: We present an approach to grammar induction that utilizes syntactic universals to improve dependency parsing across a range of languages. Our method uses a single set of manually-specified language-independent rules that identify syntactic dependencies between pairs of syntactic categories that commonly occur across languages. During inference of the probabilistic model, we use posterior expectation constraints to require that a minimum proportion of the dependencies we infer be instances of these rules. We also automatically refine the syntactic categories given in our coarsely tagged input. Across six languages our approach outperforms state-of-the-art unsupervised methods by a significant margin.

Includes members:
  Member "Using semantic cues to learn syntax":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "Reducing the Size of the Representation for the uDOP-Estimate")
  Member "Opinion Mining and Sentiment Analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "Deep Sparse Rectifier Neural Networks")
  Member "Unsupervised Dependency Parsing with Transferring Distribution via Parallel Guidance and Entropy Regularization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "The CoNLL 2007 Shared Task on Dependency Parsing")

Question: Please tell me which research area the co-authored paper (i.e., the source paper of the center hyperedge) belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science