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: "mutual information based labelling and comparing clusters"
  Abstract: After a clustering solution is generated automatically, labelling these clusters becomes important to help understanding the results. In this paper, we propose to use a mutual information based method to label clusters of journal articles. Topical terms which have the highest normalised mutual information with a certain cluster are selected to be the labels of the cluster. Discussion of the labelling technique with a domain expert was used as a check that the labels are discriminating not only lexical-wise but also semantically. Based on a common set of topical terms, we also propose to generate lexical fingerprints as a representation of individual clusters. Eventually, we visualise and compare these fingerprints of different clusters from either one clustering solution or different ones.

Connected through hyperedge HE-1 (induced by source paper "how to interpret algorithmically constructed topical structures of research specialties a case study comparing an internal and an external mapping of the topical structure of invasion biology"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "granularity of algorithmically constructed publication level classifications of research publications identification of topics"
    - "algorithmically generated subject categories based on citation relations an empirical micro study using papers on overall water splitting"
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "memetic search for overlapping topics based on a local evaluation of link communities"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "a new methodology for constructing a publication level classification system of science"

Connected through hyperedge HE-2 (induced by source paper "contextualization of topics browsing through the universe of bibliographic information"):
  with members:
    - "citation based clustering of publications using citnetexplorer and vosviewer"
    - "clustering articles based on semantic similarity"
    - "ariadne s thread interactive navigation in a world of networked information"
    - "contextualization of topics browsing through terms authors journals and cluster allocations"
    - "scientometrics and information retrieval weak links revitalized"
    - "the semantic mapping of words and co words in contexts"

Connected through hyperedge HE-3 (induced by source paper "memetic search for overlapping topics based on a local evaluation of link communities"):
  with members:
    - "citation based clustering of publications using citnetexplorer and vosviewer"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "a new methodology for constructing a publication level classification system of science"
    - "interactive overlays a new method for generating global journal maps from web of science data"

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)