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: "semi metric behavior in document networks and its application to recommendation systems"
  Abstract: Recommendation systems for different Document Networks (DN) such as the World Wide Web (WWW), Digital Libraries, or Scientific Databases, often make use of distance functions extracted from relationships among documents and between documents and semantic tags. For instance, documents in the WWW are related via a hyperlink network, while documents in bibliographic databases are related by citation and collaboration networks. Furthermore, documents can be related to semantic tags such as keywords used to describe their content. The distance functions computed from these relations establish associative networks among items of the DN, referred to as Distance Graphs, which allow recommendation systems to identify relevant associations for individual users. However, modern recommendation systems need to integrate associative data (defined by distance graphs) generated from multiple sources such as different databases, web sites, and even other users. Thus, we are presented with a problem of combining evidence (about associations between items) from different sources characterized by distance functions. In this paper we describe our work on (1) inferring relevant associations from, as well as characterizing, semi-metric distance graphs and (2) combining evidence from different distance graphs in a recommendation system. Regarding (1), we present the idea of semi-metric distance graphs, and introduce ratios to measure semi-metric behavior. We compute these ratios for several DN such as digital libraries and web sites and show that they are useful to identify implicit associations. We also propose a model based on semimetric behavior that allow us to quantify the amount of important latent associations in a DN. Regarding (2), we describe an algorithm to combine evidence from distance graphs that uses Evidence Sets, a set structure based

Connected through hyperedge HE-1 (induced by source paper "distance closures on complex networks"):
  with members:
    - "semi metric networks for recommender systems"
    - "sustaining the internet with hyperbolic mapping"
    - "uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks"
    - "mining the web for synonyms pmi ir versus lsa on toefl"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"
    - "biologically motivated distributed designs for adaptive knowledge management"

Connected through hyperedge HE-2 (induced by source paper "semi metric networks for recommender systems"):
  with members:
    - "uncovering protein interaction in abstracts and text using a novel linear model and word proximity networks"
    - "mining the web for synonyms pmi ir versus lsa on toefl"
    - "all pairs shortest paths using bridging sets and rectangular matrix multiplication"

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)