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: "similarity measures author cocitation analysis and information theory"
  Abstract: The use of Pearson's correlation coefficient in Author Cocitation Analysis was compared with Salton's cosine measure in a number of recent contributions. Unlike the Pearson correlation, the cosine is insensitive to the number of zeros. However, one has the option of applying a logarithmic transformation in correlation analysis. Information calculus is based on both the logarithmic transformation and provides a non-parametric statistics. Using this methodology one can cluster a document set in a precise way and express the differences in terms of bits of information. The algorithm is explained and used on the data set which was made the subject of this discussion.

Connected through hyperedge HE-1 (induced by source paper "the normalization of occurrence and co occurrence matrices in bibliometrics using cosine similarities and ochiai coefficients"):
  with members:
    - "journal portfolio analysis for countries cities and organizations maps and comparisons"
    - "co occurrence matrices and their applications in information science extending aca to the web environment"

Connected through hyperedge HE-2 (induced by source paper "interactive overlays a new method for generating global journal maps from web of science data"):
  with members:
    - "the structure of the arts humanities citation index a mapping on the basis of aggregated citations among 1 157 journals"
    - "a unified approach to mapping and clustering of bibliometric networks"
    - "indicators of the interdisciplinarity of journals diversity centrality and citations"
    - "a comparison of two techniques for bibliometric mapping multidimensional scaling and vos"
    - "can scientific journals be classified in terms of aggregated journal journal citation relations using the journal citation reports"
    - "an indicator of research front activity measuring intellectual organization as uncertainty reduction in document sets"
    - "science overlay maps a new tool for research policy and library management"
    - "toward alternative metrics of journal impact a comparison of download and citation data"

Connected through hyperedge HE-3 (induced by source paper "the structure and dynamics of co citation clusters a multiple perspective co citation analysis"):
  with members:
    - "towards an explanatory and computational theory of scientific discovery"
    - "lexrank graph based lexical centrality as salience in text summarization"

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)