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 "single and multi level network sparsification by algebraic distance"):
  Abstract: Network sparsification methods play an important role in modern network analysis when fast estimation of computationally expensive properties (such as the diameter, centrality indices, and paths) is required. We propose a method of network sparsification that preserves a wide range of structural properties. Depending on the analysis goals, the method allows to distinguish between local and global range edges that can be filtered out during the sparsification. First we rank edges by their algebraic distances and then we sample them. We also introduce a multilevel framework for sparsification that can be used to control the sparsification process at various coarse-grained resolutions. Based primarily on the matrix-vector multiplications, our method is easily parallelized for different architectures.

Includes members:
  Member "structure preserving sparsification of social networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncertain graph sparsification")
  Member "networkit an interactive tool suite for high performance network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient community detection in boolean composed multiplex networks")
  Member "a survey and taxonomy of graph sampling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "discovering social circles in ego networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relational thematic clustering with mutually preferred neighbors")
  Member "network sampling from static to streaming graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding the limitations of network online learning")
  Member "large social networks can be targeted for viral marketing with small seed sets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using network motifs to characterize temporal network evolution leading to diffusion inhibition")
  Member "advanced coarsening schemes for graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "relaxation based coarsening and multiscale graph organization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "recent advances in scalable network generation")

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)