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 "global rank estimation"):
  Abstract: In real world complex networks, the importance of a node depends on two important parameters: 1. characteristics of the node, and 2. the context of the given application. The current literature contains several centrality measures that have been defined to measure the importance of a node based on the given application requirements. These centrality measures assign a centrality value to each node that denotes its importance index. But in real life applications, we are more interested in the relative importance of the node that can be measured using its centrality rank based on the given centrality measure. To compute the centrality rank of a node, we need to compute the centrality value of all the nodes and compare them to get the rank. This process requires the entire network. So, it is not feasible for real-life applications due to the large size and dynamic nature of real world networks. In the present project, we aim to propose fast and efficient methods to estimate the global centrality rank of a node without computing the centrality value of all nodes. These methods can be further extended to estimate the rank without having the entire network. The proposed methods use the structural behavior of centrality measures, sampling techniques, or the machine learning models. In this work, we also discuss how to apply these methods for degree and closeness centrality rank estimation.

Includes members:
  Member "computing top k closeness centrality faster in unweighted graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "maximizing influence based group shapley centrality")
  Member "a faster method to estimate closeness centrality ranking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimating shell index in a graph with local information")
  Member "degree ranking using local information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating shell index in a graph with local information")
  Member "literature survey on interplay of topics information diffusion and connections on social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "topic lifecycle on social networks analyzing the effects of semantic continuity and social communities")
  Member "group centrality for semantic networks a swot analysis featuring random walks":
    also connected through other hyperedges:
      (none)
  Member "centrality measures in networks based on nodes attributes long range interactions and group influence":
    also connected through other hyperedges:
      (none)
  Member "rank me thou shalln t compare me":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "estimating shell index in a graph with local information")

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)