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: "finding and recommending scholarly articles"
  Abstract: The rate at which scholarly literature is being produced has been increasing at approximately 3.5 percent per year for decades. This means that during a typical 40 year career the amount of new literature produced each year increases by a factor of four. The methods scholars use to discover relevant literature must change. Just like everybody else involved in information discovery, scholars are confronted with information overload. Two decades ago, this discovery process essentially consisted of paging through abstract books, talking to colleagues and librarians, and browsing journals. A time-consuming process, which could even be longer if material had to be shipped from elsewhere. Now much of this discovery process is mediated by online scholarly information systems. All these systems are relatively new, and all are still changing. They all share a common goal: to provide their users with access to the literature relevant to their specific needs. To achieve this each system responds to actions by the user by displaying articles which the system judges relevant to the user's current needs. Recently search systems which use particularly sophisticated methodologies to recommend a few specific papers to the user have been called "recommender systems". These methods are in line with the current use of the term "recommender system" in computer science. We do not adopt this definition, rather we view systems like these as components in a larger whole, which is presented by the scholarly information systems themselves. In what follows we view the recommender system as an aspect of the entire information system; one which combines the massive memory capacities of the machine with the cognitive abilities of the human user to achieve a human-machine synergy.

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)