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 survey and taxonomy of graph sampling"):
  Abstract: Graph sampling is a technique to pick a subset of vertices and/ or edges from original graph. It has a wide spectrum of applications, e.g. survey hidden population in sociology [54], visualize social graph [29], scale down Internet AS graph [27], graph sparsification [8], etc. In some scenarios, the whole graph is known and the purpose of sampling is to obtain a smaller graph. In other scenarios, the graph is unknown and sampling is regarded as a way to explore the graph. Commonly used techniques are Vertex Sampling, Edge Sampling and Traversal Based Sampling. We provide a taxonomy of different graph sampling objectives and graph sampling approaches. The relations between these approaches are formally argued and a general framework to bridge theoretical analysis and practical implementation is provided. Although being smaller in size, sampled graphs may be similar to original graphs in some way. We are particularly interested in what graph properties are preserved given a sampling procedure. If some properties are preserved, we can estimate them on the sampled graphs, which gives a way to construct efficient estimators. If one algorithm relies on the perserved properties, we can expect that it gives similar output on original and sampled graphs. This leads to a systematic way to accelerate a class of graph algorithms. In this survey, we discuss both classical text-book type properties and some advanced properties. The landscape is tabularized and we see a lot of missing works in this field. Some theoretical studies are collected in this survey and simple extensions are made. Most previous numerical evaluation works come in an ad hoc fashion, i.e. evaluate different type of graphs, different set of properties, and different sampling algorithms. A systematical and neutral evaluation is needed to shed light on further graph sampling studies.

Includes members:
  Member "identifying communities and key vertices by reconstructing networks from samples":
    also connected through other hyperedges:
      (none)
  Member "graph size estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "true nonlinear dynamics from incomplete networks")
  Member "coarse grained topology estimation via graph sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling content distributed over graphs")
  Member "triangles to capture social cohesion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "higher order graph convolutional networks")
  Member "towards unbiased bfs sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "true nonlinear dynamics from incomplete networks")
  Member "multigraph sampling of online social networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sampling online social networks by random walk with indirect jumps")
  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 "twice ramanujan sparsifiers":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sketching transformed matrices with applications to natural language processing")

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)