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 "network sampling from static to streaming graphs"):
  Abstract: Network sampling is integral to the analysis of social, information, and biological networks. Since many real-world networks are massive in size, continuously evolving, and/or distributed in nature, the network structure is often sampled in order to facilitate study. For these reasons, a more thorough and complete understanding of network sampling is critical to support the field of network science. In this paper, we outline a framework for the general problem of network sampling, by highlighting the different objectives, population and units of interest, and classes of network sampling methods. In addition, we propose a spectrum of computational models for network sampling methods, ranging from the traditionally studied model based on the assumption of a static domain to a more challenging model that is appropriate for streaming domains. We design a family of sampling methods based on the concept of graph induction that generalize across the full spectrum of computational models (from static to streaming) while efficiently preserving many of the topological properties of the input graphs. Furthermore, we demonstrate how traditional static sampling algorithms can be modified for graph streams for each of the three main classes of sampling methods: node, edge, and topology-based sampling. Our experimental results indicate that our proposed family of sampling methods more accurately preserves the underlying properties of the graph for both static and streaming graphs. Finally, we study the impact of network sampling algorithms on the parameter estimation and performance evaluation of relational classification algorithms.

Includes members:
  Member "an in depth analysis of stochastic kronecker graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances in scalable network generation")
  Member "the role of social networks in information diffusion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning approach on information diffusion in heterogeneous networks")
  Member "space efficient sampling from social activity streams":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provable and practical approximations for the degree distribution using sublinear graph samples")
  Member "towards unbiased bfs sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "true nonlinear dynamics from incomplete networks")
  Member "benefits of bias towards better characterization of network sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "star sampling with and without replacement")
  Member "estimating and sampling graphs with multidimensional random walks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "graphact accelerating gcn training on cpu fpga heterogeneous platforms")
  Member "a comparison of techniques for sampling web pages":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "space efficient sampling from social activity streams")
  Member "a model of internet topology using k shell decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "target privacy preserving for social networks")

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)