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: "An episodic knowledge representation for narrative texts"
  Abstract: Abstract : We would like to build story understanding systems which are transparent, modular, and extensible. To this end, we have been working on a new logical approach to narrative understanding that features a GPSG-style grammar and an episodic logic with probabilistic inference rules. The grammar represents phrase structure and the relationship between phrase structure and logical form in a modular, explicit form. The logical representation allows propositional attitudes, unreliable generalizations, and other non-standard constructs, providing a uniform, transparent knowledge representation for both the explicit content of stories and for the background knowledge needed to understand them. It makes systematic use of episodic variables in the representation of episodic sentences, using these to capture temporal and causal relationships. The rules of inference include probabilistic versions of deduction rules resembling forward and backward chaining rules in expert systems. These can be used for predictive, explanatory, and simulative inference. We illustrate our approach with nontrivial grammar fragments (including semantic rules), and with an extended example of forward-chaining inference based on a sentence from Little Red Riding Hood. A pilot implementation is able to make many (though not all) of the inferences we describe. (KR)

Connected through hyperedge HE-1 (induced by source paper "BORIS -- An Experiment in In-Depth Understanding of Narratives."):
  with members:
    - "An overview of KRL, a Knowledge Representation Language"

Connected through hyperedge HE-2 (induced by source paper "TENSE TREES AS THE "FINE STRUCTURE" OF DISCOURSE"):
  with members:
    - "Discourse segmentation by human and automated means"

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