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: "edge sample and discard a new algorithm for counting triangles in large dynamic graphs"
  Abstract: Monitoring key properties of dynamic graphs in real time can help detect spam accounts in social networks, identify anomalies and intrusions, and inform resource management. Traditional frameworks for dynamic graphs have relied on processing only the stream of edges added into or deleted from an evolving graph, but not any additional related information such as the degrees or neighbor lists of nodes associated with the edges. In this paper, we propose a new edge sampling framework for big-graph analytics in dynamic graphs which enhances the traditional model and offers significantly improved trade-off between accuracy and computational/memory costs in the running estimates of a graph's properties. To demonstrate this higher accuracy using the framework, we present a new sampling algorithm, called Edge Sample and Discard, which generates an unbiased estimate of the total number of triangles in a fully dynamic graph. The experimental results show that, with the help of the neighborhood information of the sampled edges, the accuracy achieved by our algorithm is substantially better than the accuracy achieved by current state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "fleet butterfly estimation from a bipartite graph stream"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "butterfly counting in bipartite networks"
    - "on sampling from massive graph streams"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "measuring and modeling bipartite graphs with community structure"
    - "evolution of directed triangle motifs in the google osn"

Connected through hyperedge HE-2 (induced by source paper "on counting triangles through edge sampling in large dynamic graphs"):
  with members:
    - "edge based wedge sampling to estimate triangle counts in very large graphs"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "graph sample and hold a framework for big graph analytics"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "colorful triangle counting and a mapreduce implementation"

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)