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: "uniform definition of comparable and searchable information on the web"
  Abstract: Basically information means selection within a domain (value or definition set) of possibilities. For objectifiable, comparable and precise information the domain should be the same for all. Therefore the global (online) definition of the domain is proposed here. It is advantageous to define an ordered domain, because this allows using numbers for addressing the elements and because nature is ordered in many respects. The original data can be ordered in multiple independent ways. We can define a domain with multiple independent numeric dimensions to reflect this. Because we want to search information in the domain, for quantification of similarity we define a distance function or metric. Therefore we propose "Domain Spaces" (DSs) which are online defined nestable metric spaces. Their elements are called "Domain Vectors" (DVs) and have the simple form: #R##N#URL (of common DS definition) plus sequence of numbers #R##N#At this the sequence must be given so that the mapping of numbers to the DS dimensions is clear. By help of appropriate software DVs can be represented e.g. as words and numbers. Compared to words, however, DVs have (as original information) important objectifiable advantages (clear definition, objectivity, information content, range, resolution, efficiency, searchability). Using DSs users can define which information they make searchable and how it is searchable. DSs can be also used to make quantitative (numeric) data as uniform DVs interoperable, comparable and searchable. The approach is demonstrated in an online database with search engine (this http URL). The search procedure is called "Numeric Search". It consists of two systematic steps: 1. Selection of the appropriate DS e.g. by conventional word based search within the DS definitions. 2. Range and/or similarity search of DVs in the selected DS.

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)