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: "triadic measures on graphs the power of wedge sampling"
  Abstract: Graphs are used to model interactions in a variety of contexts, and there is a growing need to quickly assess the structure of a graph. Some of the most useful graph metrics, especially those measuring social cohesion, are based on triangles. Despite the importance of these triadic measures, associated algorithms can be extremely expensive. We propose a new method based on wedge sampling. This versatile technique allows for the fast and accurate approximation of all current variants of clustering coefficients and enables rapid uniform sampling of the triangles of a graph. Our methods come with provable and practical time-approximation tradeoffs for all computations. We provide extensive results that show our methods are orders of magnitude faster than the state-of-the-art, while providing nearly the accuracy of full enumeration. Our results will enable more wide-scale adoption of triadic measures for analysis of extremely large graphs, as demonstrated on several real-world examples.

Connected through hyperedge HE-1 (induced by source paper "parallel algorithms for small subgraph counting"):
  with members:
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "linear time subgraph counting graph degeneracy and the chasm at size six"
    - "exponentially faster massively parallel maximal matching"
    - "faster algorithms for edge connectivity via random 2 out contractions"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "approximating edit distance in truly subquadratic time quantum and mapreduce"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"

Connected through hyperedge HE-2 (induced by source paper "a survey on subgraph counting concepts algorithms and applications to network motifs and graphlets"):
  with members:
    - "heterogeneous network motifs"
    - "variational principle for scale free network motifs"
    - "the sketching complexity of graph and hypergraph counting"
    - "motifs in temporal networks"
    - "large scale network motif learning with compression"
    - "butterfly counting in bipartite networks"
    - "estimation of graphlet statistics"
    - "combinatorial algorithm for counting small induced graphs and orbits"

Connected through hyperedge HE-3 (induced by source paper "retrieving top weighted triangles in graphs"):
  with members:
    - "graph based semi supervised active learning for edge flows"
    - "simplicial closure and higher order link prediction"
    - "tiered sampling an efficient method for approximate counting sparse motifs in massive graph streams"
    - "edge based wedge sampling to estimate triangle counts in very large graphs"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "higher order organization of complex networks"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "approximately counting triangles in sublinear time"

Connected through hyperedge HE-4 (induced by source paper "network shrinkage estimation"):
  with members:
    - "higher order ranking and link prediction from closing triangles to closing higher order motifs"
    - "three hypergraph eigenvector centralities"
    - "on sampling from massive graph streams"
    - "representing higher order dependencies in networks"
    - "graphlet decomposition framework algorithms and applications"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "graph sample and hold a framework for big graph analytics"
    - "near optimal entrywise sampling for data matrices"

Connected through hyperedge HE-5 (induced by source paper "a sampling framework for counting temporal motifs"):
  with members:
    - "a chronological edge driven approach to temporal subgraph isomorphism"
    - "motifs in temporal networks"
    - "enumerating maximal cliques in link streams with durations"
    - "when is a network a network multi order graphical model selection in pathways and temporal networks"
    - "science askscience and badscience on the coexistence of highly related communities"
    - "higher order organization of complex networks"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "scalable motif aware graph clustering"

Connected through hyperedge HE-6 (induced by source paper "efficient butterfly counting for large bipartite networks"):
  with members:
    - "a survey of community search over big graphs"
    - "butterfly counting in bipartite networks"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "compressing graphs and indexes with recursive graph bisection"
    - "measuring and modeling bipartite graphs with community structure"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-7 (induced by source paper "vertex priority based butterfly counting for large scale bipartite networks"):
  with members:
    - "a survey of community search over big graphs"
    - "peeling bipartite networks for dense subgraph discovery"
    - "compressing graphs and indexes with recursive graph bisection"
    - "measuring and modeling bipartite graphs with community structure"
    - "when engagement meets similarity efficient k r core computation on social networks"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-8 (induced by source paper "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "a scalable generative graph model with community structure"
    - "multi level anomaly detection on time varying graph data"
    - "community structure and scale free collections of erdős renyi graphs"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "systematic topology analysis and generation using degree correlations"

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)