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: "learning better context characterizations an intelligent information retrieval approach"
  Abstract: Grupo de Investigacio´n en Recuperacio´n de Informacio´n y Gestio´n del ConocimientoLIDIA - Laboratorio de Investigacio´n y Desarrollo en Inteligencia ArtiﬁcialDepartamento de Ciencias e Ingenier´ia de la Computacio´nUniversidad Nacional del Sur, Av. Alem 1253, (B8000CPB) Bah´ia Blanca, ArgentinaCONICET - Consejo Nacional de Investigaciones Cient´iﬁcas y Te´cnicasphone: 54-291-4595135 fax: 54-291-4595136e-mail:{cml,agm}@cs.uns.edu.arAbstract. This paper proposes an incremental method that can be used by anintelligent system to learn better descriptions of a thematic context. The methodstarts with a small number of terms selected from a simple description of the topicunder analysis and uses this description as the initial search context. Using theseterms, a set of queries are built and submitted to a search engine. New documentsand terms are used to reﬁne the learned vocabulary. Evaluati ons performed on alarge number of topics indicate that the learned vocabulary is much more effec-tive than the original one at the time of constructing queries to retrieve relevantmaterial.

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)