human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "incremental algorithms for network management and analysis based on closeness centrality":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "global rank estimation")
  Member "betweenness centrality incremental and faster":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an adaptive version of brandes algorithm for betweenness centrality")
  Member "graph size estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "true nonlinear dynamics from incomplete networks")
  Member "how to make the top ten approximating pagerank from in degree":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "index wiki database design and experiments")
  Member "fast approximation of centrality":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic approximate shortest paths and beyond subquadratic and worst case update time")

Question: Please tell me which class the source paper 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)