human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "relevance as deduction a logical view of information retrieval"
  Abstract: The problem of Information Retrieval is, given a set of documents D and a query q, providing an algorithm for retrieving all documents in D relevant to q. However, retrieval should depend and be updated whenever the user is able to provide as an input a preferred set of relevant documents; this process is known as em relevance feedback. Recent work in IR has been paying great attention to models which employ a logical approach; the advantage being that one can have a simple computable characterization of retrieval on the basis of a pure logical analysis of retrieval. Most of the logical models make use of probabilities or similar belief functions in order to introduce the inductive component whereby uncertainty is treated. Their general paradigm is the following: em find the nature of conditional $d\imp q$ and then define a probability on the top of it. We just reverse this point of view; first use the numerical information, frequencies or probabilities, then define your own logical consequence. More generally, we claim that retrieval is a form of deduction. We introduce a simple but powerful logical framework of relevance feedback, derived from the well founded area of nonmonotonic logic. This description can help us evaluate, describe and compare from a theoretical point of view previous approaches based on conditionals or probabilities.

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IR(Information Retrieval)