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: "characterizing interdisciplinarity of researchers and research topics using web search engines"
  Abstract: Researchers’ networks have been subject to active modeling and analysis. Earlier literature mostly focused on citation or coauthorship networks reconstructed from annotated scientific publication databases, which have several limitations. Recently, general-purpose web search engines have also been utilized to collect information about social networks. Here we reconstructed, using web search engines, a network representing the relatedness of researchers to their peers as well as to various research topics. Relatedness between researchers and research topics was characterized by visibility boost—increase of a researcher’s visibility by focusing on a particular topic. It was observed that researchers who had high visibility boosts by the same research topic tended to be close to each other in their network. We calculated correlations between visibility boosts by research topics and researchers’ interdisciplinarity at the individual level (diversity of topics related to the researcher) and at the social level (his/her centrality in the researchers’ network). We found that visibility boosts by certain research topics were positively correlated with researchers’ individual-level interdisciplinarity despite their negative correlations with the general popularity of researchers. It was also found that visibility boosts by network-related topics had positive correlations with researchers’ social-level interdisciplinarity. Research topics’ correlations with researchers’ individual- and social-level interdisciplinarities were found to be nearly independent from each other. These findings suggest that the notion of ‘‘interdisciplinarity’’ of a researcher should be understood as a multi-dimensional concept that should be evaluated using multiple assessment means.

Connected through hyperedge HE-1 (induced by source paper "using text analysis to quantify the similarity and evolution of scientific disciplines"):
  with members:
    - "the ground truth about metadata and community detection in networks"
    - "using network science and text analytics to produce surveys in a scientific topic"
    - "science concierge a fast content based recommendation system for scientific publications"
    - "inheritance patterns in citation networks reveal scientific memes"
    - "quantifying long term scientific impact"

Connected through hyperedge HE-2 (induced by source paper "role of interdisciplinarity in computer sciences quantification impact and life trajectory"):
  with members:
    - "the academic social network"

Connected through hyperedge HE-3 (induced by source paper "quantifying the consistency of scientific databases"):
  with members:
    - "network based statistical comparison of citation topology of bibliographic databases"
    - "model of complex networks based on citation dynamics"
    - "quantifying long term scientific impact"
    - "an expert system for detecting automobile insurance fraud using social network analysis"
    - "betweenness centrality as a driver of preferential attachment in the evolution of research collaboration networks"

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.SI(Social and Information Networks)