human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "clique counting in mapreduce algorithms and experiments"):
  Abstract: We tackle the problem of counting the number qk of k-cliques in large-scale graphs, for any constant k ≥ 3. Clique counting is essential in a variety of applications, including social network analysis. Our algorithms make it possible to compute qk for several real-world graphs and shed light on its growth rate as a function of k. Even for small values of k, the number qk of k-cliques can be in the order of tens or hundreds of trillions. As k increases, different graph instances show different behaviors: while on some graphs qk + 1    Due to the computationally intensive nature of the clique counting problem, we settle for parallel solutions in the MapReduce framework, which has become in the last few years a de facto standard for batch processing of massive datasets. We give both theoretical and experimental contributions.   On the theory side, we design the first exact scalable algorithm for counting (and listing) k-cliques in MapReduce. Our algorithm uses O(m3/2) total space and O(mk/2) work, where m is the number of graph edges. This matches the best-known bounds for triangle listing when k e 3 and is work optimal in the worst case for any k, while keeping the communication cost independent of k. We also design sampling-based estimators that can dramatically reduce the running time and space requirements of the exact approach, while providing very accurate solutions with high probability.   We then assess the effectiveness of different clique counting approaches through an extensive experimental analysis over the Amazon EC2 platform, considering both our algorithms and their state-of-the-art competitors. The experimental results clearly highlight the algorithm of choice in different scenarios and prove our exact approach to be the most effective when the number of k-cliques is large, gracefully scaling to nontrivial values of k even on clusters of small/medium size. Our approximation algorithms achieve extremely accurate estimates and large speedups, especially on the toughest instances for the exact algorithms.

Includes members:
  Member "subgraph enumeration in massive graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "worst case optimal algorithms for parallel query processing")
  Member "the input output complexity of triangle enumeration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "instance and output optimal parallel algorithms for acyclic joins")
  Member "a novel approach to finding near cliques the triangle densest subgraph problem":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "near optimal fully dynamic densest subgraph")
  Member "enumerating subgraph instances using map reduce":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of community search over big graphs")
  Member "space round tradeoffs for mapreduce computations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "efficient triangle counting in large graphs via degree based vertex partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "sorting searching and simulation in the mapreduce framework":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "colorful triangle counting and a mapreduce implementation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parallel algorithms for small subgraph counting")

Question: Please tell me which class the source paper 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.DC(Distributed, Parallel, and Cluster Computing)