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 "a faster method to estimate closeness centrality ranking"):
  Abstract: Closeness centrality is one way of measuring how central a node is in the given network. The closeness centrality measure assigns a centrality value to each node based on its accessibility to the whole network. In real life applications, we are mainly interested in ranking nodes based on their centrality values. The classical method to compute the rank of a node first computes the closeness centrality of all nodes and then compares them to get its rank. Its time complexity is $O(n \cdot m + n)$, where $n$ represents total number of nodes, and $m$ represents total number of edges in the network. In the present work, we propose a heuristic method to fast estimate the closeness rank of a node in $O(\alpha \cdot m)$ time complexity, where $\alpha = 3$. We also propose an extended improved method using uniform sampling technique. This method better estimates the rank and it has the time complexity $O(\alpha \cdot m)$, where $\alpha \approx 10-100$. This is an excellent improvement over the classical centrality ranking method. The efficiency of the proposed methods is verified on real world scale-free social networks using absolute and weighted error functions.

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 "degree ranking using local information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "estimating shell index in a graph with local information")
  Member "rank me thou shalln t compare me":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "estimating shell index in a graph with local information")
  Member "computing classic closeness centrality at scale":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "machine learning in network centrality measures tutorial and outlook")
  Member "incremental algorithms for network management and analysis based on closeness centrality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "global rank estimation")
  Member "social dynamics of digg":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep collaborative embedding for information cascade prediction")
  Member "applying centrality measures to impact analysis a coauthorship network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "machine learning in network centrality measures tutorial and outlook")

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)