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: "wedge sampling for computing clustering coefficients and triangle counts on large graphs"
  Abstract: Graphs are used to model interactions in a variety of contexts, and there is a growing need to quickly assess the structure of such graphs. Some of the most useful graph metrics are based on triangles, such as those measuring social cohesion. Algorithms to compute them can be extremely expensive, even for moderately-sized graphs with only millions of edges. Previous work has considered node and edge sampling; in contrast, we consider wedge sampling, which provides faster and more accurate approximations than competing techniques. Additionally, wedge sampling enables estimation local clustering coefficients, degree-wise clustering coefficients, uniform triangle sampling, and directed triangle counts. Our methods come with provable and practical probabilistic error estimates for all computations. We provide extensive results that show our methods are both more accurate and faster than state-of-the-art alternatives.

Connected through hyperedge HE-1 (induced by source paper "the power of pivoting for exact clique counting"):
  with members:
    - "higher order clustering in networks"
    - "detecting strong ties using network motifs"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "clique counting in mapreduce algorithms and experiments"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"

Connected through hyperedge HE-2 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics"):
  with members:
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "active access a mechanism for high performance distributed data centric computations"
    - "communication efficient jaccard similarity for high performance distributed genome comparisons"
    - "local computation algorithms for spanners"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"

Connected through hyperedge HE-3 (induced by source paper "efficiently counting vertex orbits of all 5 vertex subgraphs by evoke"):
  with members:
    - "heterogeneous network motifs"
    - "higher order clustering in networks"
    - "detecting strong ties using network motifs"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "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-4 (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-5 (induced by source paper "rept a streaming algorithm of approximating global and local triangle counts in parallel"):
  with members:
    - "on sampling from massive graph streams"
    - "ringo interactive graph analytics on big memory machines"
    - "nxgraph an efficient graph processing system on a single machine"
    - "the input output complexity of triangle enumeration"
    - "graph sample and hold a framework for big graph analytics"
    - "parallel triangle counting in massive streaming graphs"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "image labeling on a network using social network metadata for image classification"

Connected through hyperedge HE-6 (induced by source paper "butterfly counting in bipartite networks"):
  with members:
    - "edge based wedge sampling to estimate triangle counts in very large graphs"
    - "peeling bipartite networks for dense subgraph discovery"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "a fast and provable method for estimating clique counts using tur an s theorem"
    - "measuring and modeling bipartite graphs with community structure"
    - "graphlet decomposition framework algorithms and applications"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "triadic measures on graphs the power of wedge sampling"

Connected through hyperedge HE-7 (induced by source paper "edge based wedge sampling to estimate triangle counts in very large graphs"):
  with members:
    - "counting triangles in massive graphs with mapreduce"
    - "powers of tensors and fast matrix multiplication"
    - "triadic measures on graphs the power of wedge sampling"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "uncovering social network sybils in the wild"
    - "colorful triangle counting and a mapreduce implementation"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-8 (induced by source paper "on the complexity of sampling nodes uniformly from a graph"):
  with members:
    - "how large is your graph"
    - "on sampling edges almost uniformly"
    - "simple set cardinality estimation through random sampling"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

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)