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: "estimating the degree centrality ranking of a node"
  Abstract: Complex networks have gained more attention from the last few years. The size of the real world complex networks, such as online social networks, WWW networks, collaboration networks, is exponentially increasing with time. It is not feasible to completely collect, store and process these networks. In the present work, we propose a method to estimate the degree centrality ranking of a node without having complete structure of the graph. The proposed algorithm uses degree of a node and power law exponent of the degree distribution to calculate the ranking. We also study simulation results on Barabasi-Albert model. Simulation results show that the average error in the estimated ranking is approximately $5\%$ of the total number of nodes.

Connected through hyperedge HE-1 (induced by source paper "estimating shell index in a graph with local information"):
  with members:
    - "global rank estimation"
    - "a faster method to estimate closeness centrality ranking"
    - "degree ranking using local information"
    - "modeling memetics using edge diversity"
    - "pseudo cores the terminus of an intelligent viral meme s trajectory"
    - "rank me thou shalln t compare me"
    - "evolving models for meso scale structures"
    - "understanding spreading patterns on social networks based on network topology"

Connected through hyperedge HE-2 (induced by source paper "global rank estimation"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "a faster method to estimate closeness centrality ranking"
    - "degree ranking using local information"
    - "literature survey on interplay of topics information diffusion and connections on social networks"
    - "group centrality for semantic networks a swot analysis featuring random walks"
    - "centrality measures in networks based on nodes attributes long range interactions and group influence"
    - "rank me thou shalln t compare me"
    - "understanding spreading patterns on social networks based on network topology"

Connected through hyperedge HE-3 (induced by source paper "degree ranking using local information"):
  with members:
    - "a faster method to estimate closeness centrality ranking"
    - "ant inspired density estimation via random walks"
    - "sublinear time estimation of degree distribution moments the arboricity connection"
    - "rank me thou shalln t compare me"
    - "understanding spreading patterns on social networks based on network topology"
    - "walk not wait faster sampling over online social networks"
    - "real time enhanced random sampling of online social networks"
    - "graph size estimation"

Connected through hyperedge HE-4 (induced by source paper "a faster method to estimate closeness centrality ranking"):
  with members:
    - "computing top k closeness centrality faster in unweighted graphs"
    - "degree ranking using local information"
    - "rank me thou shalln t compare me"
    - "computing classic closeness centrality at scale"
    - "incremental algorithms for network management and analysis based on closeness centrality"
    - "social dynamics of digg"
    - "applying centrality measures to impact analysis a coauthorship network analysis"
    - "fast approximation of centrality"

Connected through hyperedge HE-5 (induced by source paper "rank me thou shalln t compare me"):
  with members:
    - "modeling memetics using edge diversity"
    - "evolving models for meso scale structures"
    - "understanding spreading patterns on social networks based on network topology"
    - "graph size estimation"

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)