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 "estimating node similarity by sampling streaming bipartite graphs"):
  Abstract: Bipartite graph data increasingly occurs as a stream of edges that represent transactions, e.g., purchases by retail customers. Applications such as recommender systems employ neighborhood-based measures of node similarity, such as the pairwise number of common neighbors (CN) and related metrics. While the number of node pairs that share neighbors is potentially enormous, in real-word graphs only a relatively small proportion of all pairs have a large number of common neighbors. This motivates finding a weighted sampling approach that preferentially samples such node pairs. #R##N#This paper presents a new sampling algorithm that provides a fixed size unbiased estimate of the similarity (or projected) graph on a bipartite edge stream. The algorithm has two components. First, it maintains a reservoir of sampled bipartite edges with sampling weights that favor selection of high similarity nodes. Second, arriving edges generate a stream of similarity updates based on their adjacency with the current sample. These updates are aggregated in a second reservoir sample-based stream aggregator to yield the final unbiased estimate. Experiments on real world graphs show that a 10% sample ar each stages yields estimates of high similarity edges with weighted relative errors of about 10^{-2}

Includes members:
  Member "stream aggregation through order sampling":
    also connected through other hyperedges:
      (none)
  Member "on sampling from massive graph streams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hypergraph motifs concepts algorithms and discoveries")
  Member "graph sample and hold a framework for big graph analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep graph similarity learning a survey")
  Member "network sampling from static to streaming graphs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding the limitations of network online learning")
  Member "streaming algorithms from precision sampling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal sketching for kronecker product regression and low rank approximation")
  Member "the link prediction problem in bipartite networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bipartite link prediction based on topological features via 2 hop path")
  Member "stream sampling for variance optimal estimation of subset sums":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the adversarial robustness of sampling")

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)