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: "parallel triangle counting in massive streaming graphs"
  Abstract: The number of triangles in a graph is a fundamental metric, used in social network analysis, link classification and recommendation, and more. Driven by these applications and the trend that modern graph datasets are both large and dynamic, we present the design and implementation of a fast and cache-efficient parallel algorithm for estimating the number of triangles in a massive undirected graph whose edges arrive as a stream. It brings together the benefits of streaming algorithms and parallel algorithms. By building on the streaming algorithms framework, the algorithm has a small memory footprint. By leveraging the paralell cache-oblivious framework, it makes efficient use of the memory hierarchy of modern multicore machines without needing to know its specific parameters. We prove theoretical bounds on accuracy, memory access cost, and parallel runtime complexity, as well as showing empirically that the algorithm yields accurate results and substantial speedups compared to an optimized sequential implementation. #R##N#(This is an expanded version of a CIKM'13 paper of the same title.)

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"
    - "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-2 (induced by source paper "parallel algorithms for butterfly computations"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "vertex priority based butterfly counting for large scale bipartite networks"
    - "butterfly counting in bipartite networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "parallel streaming random sampling"):
  with members:
    - "efficient parallel random sampling vectorized cache efficient and online"

Connected through hyperedge HE-4 (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"

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"
    - "wedge sampling for computing clustering coefficients and triangle counts on large 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 "cocos fast and accurate distributed triangle counting in graph streams"):
  with members:
    - "on sampling from massive graph streams"
    - "wrs waiting room sampling for accurate triangle counting in real graph streams"
    - "a hybrid sampling scheme for triangle counting"
    - "graph sample and hold a framework for big graph analytics"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "defining and evaluating network communities based on ground truth"
    - "colorful triangle counting and a mapreduce implementation"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-7 (induced by source paper "dislr distributed sampling with limited redundancy for triangle counting in graph streams"):
  with members:
    - "on sampling from massive graph streams"
    - "graph sample and hold a framework for big graph analytics"

Connected through hyperedge HE-8 (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"
    - "wedge sampling for computing clustering coefficients and triangle counts on large graphs"

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.DB(Databases)