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 "coarse grained topology estimation via graph sampling"):
  Abstract: Many online networks are measured and studied via sampling techniques, which typically collect a relatively small fraction of nodes and their associated edges. Past work in this area has primarily focused on obtaining a representative sample of nodes and on efficient estimation of local graph properties (such as node degree distribution or any node attribute) based on that sample. However, less is known about estimating the global topology of the underlying graph. #R##N#In this paper, we show how to efficiently estimate the coarse-grained topology of a graph from a probability sample of nodes. In particular, we consider that nodes are partitioned into categories (e.g., countries or work/study places in OSNs), which naturally defines a weighted category graph. We are interested in estimating (i) the size of categories and (ii) the probability that nodes from two different categories are connected. For each of the above, we develop a family of estimators for design-based inference under uniform or non-uniform sampling, employing either of two measurement strategies: induced subgraph sampling, which relies only on information about the sampled nodes; and star sampling, which also exploits category information about the neighbors of sampled nodes. We prove consistency of these estimators and evaluate their efficiency via simulation on fully known graphs. We also apply our methodology to a sample of Facebook users to obtain a number of category graphs, such as the college friendship graph and the country friendship graph; we share and visualize the resulting data at www.geosocialmap.com.

Includes members:
  Member "social structure of facebook networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering")
  Member "graph size estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "true nonlinear dynamics from incomplete networks")
  Member "walking on a graph with a magnifying glass stratified sampling via weighted random walks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "star sampling with and without replacement")
  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 "compressive sensing over graphs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural network tomography")
  Member "rhythms of social interaction messaging within a massive online network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "designing for health chatbots")

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)