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: "rank me thou shalln t compare me"
  Abstract: Centrality measures have been defined to quantify the importance of a node in complex networks. The relative importance of a node can be measured using its centrality rank based on the centrality value. In the present work, we predict the degree centrality rank of a node without having the entire network. The proposed method uses degree of the node and some network parameters to predict its rank. These network parameters include network size, minimum, maximum, and average degree of the network. These parameters are estimated using random walk sampling techniques. The proposed method is validated on Barabasi-Albert networks. Simulation results show that the proposed method predicts the rank of higher degree nodes with more accuracy. The average error in the rank prediction is approximately $0.16\%$ of the network size.

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"
    - "evolving models for meso scale structures"
    - "understanding spreading patterns on social networks based on network topology"
    - "estimating the degree centrality ranking of a node"

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"
    - "understanding spreading patterns on social networks based on network topology"
    - "fully dynamic approximation of betweenness centrality"

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"
    - "understanding spreading patterns on social networks based on network topology"
    - "estimating the degree centrality ranking of a node"
    - "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"
    - "estimating the degree centrality ranking of a node"
    - "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"

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)