human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "walk not wait faster sampling over online social networks"
  Abstract: In this paper, we introduce a novel, general purpose, technique for faster sampling of nodes over an online social network. Specifically, unlike traditional random walk which wait for the convergence of sampling distribution to a predetermined target distribution - a waiting process that incurs a high query cost - we develop WALK-ESTIMATE, which starts with a much shorter random walk, and then proactively estimate the sampling probability for the node taken before using acceptance-rejection sampling to adjust the sampling probability to the predetermined target distribution. We present a novel backward random walk technique which provides provably unbiased estimations for the sampling probability, and demonstrate the superiority of WALK-ESTIMATE over traditional random walks through theoretical analysis and extensive experiments over real world online social networks.

Connected through hyperedge HE-1 (induced by source paper "optimizing item and subgroup configurations for social aware vr shopping"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "neural collaborative filtering"
    - "spmc socially aware personalized markov chains for sparse sequential recommendation"
    - "a multilayer network dataset of interaction and influence spreading in a virtual world"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "submodular and supermodular multi labeling and vertex happiness"
    - "vbpr visual bayesian personalized ranking from implicit feedback"
    - "from group recommendations to group formation"

Connected through hyperedge HE-2 (induced by source paper "degree ranking using local information"):
  with members:
    - "a faster method to estimate closeness centrality ranking"
    - "ant inspired density estimation via random walks"
    - "sublinear time estimation of degree distribution moments the arboricity connection"
    - "rank me thou shalln t compare me"
    - "understanding spreading patterns on social networks based on network topology"
    - "estimating the degree centrality ranking of a node"
    - "real time enhanced random sampling of online social networks"
    - "graph size estimation"

Connected through hyperedge HE-3 (induced by source paper "topic based influence computation in social networks under resource constraints"):
  with members:
    - "can cascades be predicted"
    - "uncovering the temporal dynamics of diffusion networks"
    - "supervised random walks predicting and recommending links in social networks"
    - "on the convexity of latent social network inference"
    - "predicting the popularity of online content"

Question: Please tell me which class the center node 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)