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 "degree ranking using local information"):
  Abstract: Most real world dynamic networks are evolved very fast with time. It is not feasible to collect the entire network at any given time to study its characteristics. This creates the need to propose local algorithms to study various properties of the network. In the present work, we estimate degree rank of a node without having the entire network. The proposed methods are based on the power law degree distribution characteristic or sampling techniques. The proposed methods are simulated on synthetic networks, as well as on real world social networks. The efficiency of the proposed methods is evaluated using absolute and weighted error functions. Results show that the degree rank of a node can be estimated with high accuracy using only $1\%$ samples of the network size. The accuracy of the estimation decreases from high ranked to low ranked nodes. We further extend the proposed methods for random networks and validate their efficiency on synthetic random networks, that are generated using Erdős-Renyi model. Results show that the proposed methods can be efficiently used for random networks as well.

Includes members:
  Member "a faster method to estimate closeness centrality ranking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "estimating shell index in a graph with local information")
  Member "ant inspired density estimation via random walks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "sublinear time estimation of degree distribution moments the arboricity connection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "independent set size approximation in graph streams")
  Member "rank me thou shalln t compare me":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "estimating shell index in a graph with local information")
  Member "understanding spreading patterns on social networks based on network topology":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "estimating shell index in a graph with local information")
  Member "estimating the degree centrality ranking of a node":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "estimating shell index in a graph with local information")
  Member "walk not wait faster sampling over online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping")
  Member "real time enhanced random sampling of online social networks":
    also connected through other hyperedges:
      (none)

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)