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: "space efficient sampling from social activity streams"
  Abstract: In order to efficiently study the characteristics of network domains and support development of network systems (e.g. algorithms, protocols that operate on networks), it is often necessary to sample a representative subgraph from a large complex network. Although recent subgraph sampling methods have been shown to work well, they focus on sampling from memory-resident graphs and assume that the sampling algorithm can access the entire graph in order to decide which nodes/edges to select. Many large-scale network datasets, however, are too large and/or dynamic to be processed using main memory (e.g., email, tweets, wall posts). In this work, we formulate the problem of sampling from large graph streams. We propose a streaming graph sampling algorithm that dynamically maintains a representative sample in a reservoir based setting. We evaluate the efficacy of our proposed methods empirically using several real-world data sets. Across all datasets, we found that our method produce samples that preserve better the original graph distributions.

Connected through hyperedge HE-1 (induced by source paper "provable and practical approximations for the degree distribution using sublinear graph samples"):
  with members:
    - "measuring and modeling bipartite graphs with community structure"
    - "sublinear time estimation of degree distribution moments the degeneracy connection"
    - "maxoutprobe an algorithm for increasing the size of partially observed networks"
    - "approximately counting triangles in sublinear time"
    - "catching the head tail and everything in between a streaming algorithm for the degree distribution"
    - "the power of an example hidden set size approximation using group queries and conditional sampling"
    - "graph sample and hold a framework for big graph analytics"
    - "network sampling from static to streaming graphs"

Connected through hyperedge HE-2 (induced by source paper "catching the head tail and everything in between a streaming algorithm for the degree distribution"):
  with members:
    - "graph sample and hold a framework for big graph analytics"
    - "approximating large frequency moments with pick and drop sampling"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "network sampling from static to streaming graphs"
    - "community structure and scale free collections of erdős renyi graphs"

Connected through hyperedge HE-3 (induced by source paper "graphlet decomposition framework algorithms and applications"):
  with members:
    - "role discovery in networks"
    - "graph sample and hold a framework for big graph analytics"
    - "subgraph frequencies mapping the empirical and extremal geography of large graph collections"
    - "network sampling from static to streaming graphs"
    - "social structure of facebook networks"

Connected through hyperedge HE-4 (induced by source paper "network sampling from static to streaming graphs"):
  with members:
    - "an in depth analysis of stochastic kronecker graphs"
    - "the role of social networks in information diffusion"
    - "towards unbiased bfs sampling"
    - "benefits of bias towards better characterization of network sampling"
    - "estimating and sampling graphs with multidimensional random walks"
    - "a comparison of techniques for sampling web pages"
    - "a model of internet topology using k shell decomposition"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

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)