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: "efficient triangle counting in large graphs via degree based vertex partitioning"
  Abstract: In this paper we present an efficient triangle counting algorithm which can be adapted to the semistreaming model [12]. The key idea of our algorithm is to combine the sampling algorithm of [31,32] and the partitioning of the set of vertices into a high degree and a low degree subset respectively as in [1], treating each set appropriately. We obtain a running time \(O \left( m + \frac{m^{3/2} \Delta \log{n} }{t \epsilon^2} \right)\) and an e approximation (multiplicative error), where n is the number of vertices, m the number of edges and Δ the maximum number of triangles an edge is contained. Furthermore, we show how this algorithm can be adapted to the semistreaming model with space usage \(O\left(m^{1/2}\log{n} + \frac{m^{3/2} \Delta \log{n}}{t \epsilon^2} \right)\) and a constant number of passes (three) over the graph stream. We apply our methods in various networks with several millions of edges and we obtain excellent results. Finally, we propose a random projection based method for triangle counting and provide a sufficient condition to obtain an estimate with low variance.

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 "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-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 "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"
    - "edge sample and discard a new algorithm for counting triangles in large dynamic graphs"
    - "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"

Connected through hyperedge HE-5 (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"
    - "triadic measures on graphs the power of wedge sampling"

Connected through hyperedge HE-6 (induced by source paper "distributed algorithms for subgraph centric graph platforms"):
  with members:
    - "elastic partition placement for non stationary graph algorithms"
    - "goffish a sub graph centric framework for large scale graph analytics"

Connected through hyperedge HE-7 (induced by source paper "the sketching complexity of graph and hypergraph counting"):
  with members:
    - "size bounds and query plans for relational joins"
    - "estimating graph parameters from random order streams"
    - "on estimating maximum matching size in graph streams"
    - "on the multiparty communication complexity of testing triangle freeness"
    - "escape efficiently counting all 5 vertex subgraphs"
    - "on approximating functions of the singular values in a stream"
    - "kernelization via sampling with applications to dynamic graph streams"
    - "tight bounds for linear sketches of approximate matchings"

Connected through hyperedge HE-8 (induced by source paper "counting butterfies from a large bipartite graph stream"):
  with members:
    - "parallel algorithms for butterfly computations"
    - "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"
    - "path sampling a fast and provable method for estimating 4 vertex subgraph counts"
    - "triangle counting in dynamic graph streams"

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.DS(Data Structures and Algorithms)