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: "on the fly query entity decomposition using snippets"
  Abstract: AbstractOne of the most important issues in Information Retrieval is inferringthe intents underlying users’ queries. Thus, any tool to enrich or to bettercontextualized queries can proof extremely valuable. Entity extraction,provided it is done fast, can be one of such tools. Such techniques usuallyrely on a prior training phase involving large datasets. That training iscostly, specially in environments which are increasingly moving towardsreal time scenarios where latency to retrieve fresh informacion should beminimal. In this paper an ‘on-the-ﬂy’ query decomposition method isproposed. It uses snippets which are mined by means of a nave statisticalalgorithm. An initial evaluation of such a method is provided, in additionto a discussion on its applicability to diﬀerent scenarios. 1 Introduction One of the main purposes in Web Information Retrieval is helping users tofulﬁll their information needs. However, most of the time, the main problemis determining the users’ intent. For instance, is s/he looking for a particularresult? A comparison between some results? Does the input provide any clueabout the goal or the intent behind the query? Has any other user issued thesame querybefore? Nonetheless to say, interpreting the users’ input is a complextask that can be improved by using a number of diﬀerent techniques.Thus, the content the users provide to the IR system can be analyzed (e.g.the queries they submit to a search engine). This way it is possible to opti-mize the results or to provide some feedback which the users can asses in theirinformation retrieval process (e.g. a search engine could provide more focusedresults or query suggestions).In the late 1990s seminal studies analyzing Web user’s information retrievalprocess (e.g. [6] or [?]) reported important diﬀerences from users in other in-formation retrieval scenarios. Those papers also included a categorization of

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)