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 "sampling on networks estimating spectral centrality measures and their impact in evaluating other relevant network measures"):
  Abstract: We perform an extensive analysis of how sampling impacts the estimate of several relevant network measures. #R##N#In particular, we focus on how a sampling strategy optimized to recover a particular spectral centrality measure impacts other topological quantities. Our goal is on one hand to extend the analysis of the behavior of TCEC [Ruggeri2019], a theoretically-grounded sampling method for eigenvector centrality estimation. #R##N#On the other hand, to demonstrate more broadly how sampling can impact the estimation of relevant network properties like centrality measures different than the one aimed at optimizing, community structure and node attribute distribution. #R##N#Finally, we adapt the theoretical framework behind TCEC for the case of PageRank centrality and propose a sampling algorithm aimed at optimizing its estimation. We show that, while the theoretical derivation can be suitably adapted to cover this case, the resulting algorithm suffers of a high computational complexity that requires further approximations compared to the eigenvector centrality case.

Includes members:
  Member "blind inference of centrality rankings from graph signals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "exact blind community detection from signals on multiple graphs")
  Member "towards quantifying sampling bias in network inference":
    also connected through other hyperedges:
      (none)
  Member "sampling from social networks with attributes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a total error framework for digital traces of humans")
  Member "node2vec scalable feature learning for networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep graph matching consensus")
  Member "stability and continuity of centrality measures in weighted graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph signal processing part ii processing and analyzing signals on graphs")
  Member "is the sample good enough comparing data from twitter s streaming api with twitter s firehose":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on bias and fairness in machine learning")
  Member "community structure in large networks natural cluster sizes and the absence of large well defined clusters":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "certified robustness of community detection against adversarial structural perturbation via randomized smoothing")

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)