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: "enumerating subgraph instances using map reduce"
  Abstract: The theme of this paper is how to find all instances of a given "sample" graph in a larger "data graph," using a single round of map-reduce. For the simplest sample graph, the triangle, we improve upon the best known such algorithm. We then examine the general case, considering both the communication cost between mappers and reducers and the total computation cost at the reducers. To minimize communication cost, we exploit the techniques of (Afrati and Ullman, TKDE 2011)for computing multiway joins (evaluating conjunctive queries) in a single map-reduce round. Several methods are shown for translating sample graphs into a union of conjunctive queries with as few queries as possible. We also address the matter of optimizing computation cost. Many serial algorithms are shown to be "convertible," in the sense that it is possible to partition the data graph, explore each partition in a separate reducer, and have the total computation cost at the reducers be of the same order as the computation cost of the serial algorithm.

Connected through hyperedge HE-1 (induced by source paper "a survey of community search over big graphs"):
  with members:
    - "exploring communities in large profiled graphs"
    - "efficient algorithms for densest subgraph discovery"
    - "community discovery in dynamic networks a survey"
    - "pp dblp modeling and generating attributed public private networks with dblp"
    - "an optimal and progressive approach to online search of top k influential communities"
    - "local community detection in dynamic networks"
    - "diversified coherent core search on multi layer graphs"
    - "network community detection a review and visual survey"

Connected through hyperedge HE-2 (induced by source paper "a survey and experimental analysis of distributed subgraph matching"):
  with members:
    - "foundations of modern query languages for graph databases"
    - "a selectivity based approach to continuous pattern detection in streaming graphs"
    - "communication steps for parallel query processing"
    - "efficient subgraph matching on billion node graphs"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-3 (induced by source paper "fast and robust distributed subgraph enumeration"):
  with members:
    - "experimental analysis of distributed graph systems"

Connected through hyperedge HE-4 (induced by source paper "security and privacy aspects in mapreduce on clouds a survey"):
  with members:
    - "assignment problems of different sized inputs in mapreduce"
    - "computing marginals using mapreduce"
    - "meta mapreduce a technique for reducing communication in mapreduce computations"
    - "on the computational complexity of mapreduce"
    - "the family of mapreduce and large scale data processing systems"
    - "efficient processing of k nearest neighbor joins using mapreduce"
    - "efficient multi way theta join processing using mapreduce"
    - "vision paper towards an understanding of the limits of map reduce computation"

Connected through hyperedge HE-5 (induced by source paper "a fast and provable method for estimating clique counts using tur an s theorem"):
  with members:
    - "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"
    - "community structure and scale free collections of erdős renyi graphs"
    - "listing all maximal cliques in large sparse real world graphs"

Connected through hyperedge HE-6 (induced by source paper "clique counting in mapreduce algorithms and experiments"):
  with members:
    - "subgraph enumeration in massive graphs"
    - "the input output complexity of triangle enumeration"
    - "a novel approach to finding near cliques the triangle densest subgraph problem"
    - "space round tradeoffs for mapreduce computations"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "sorting searching and simulation in the mapreduce framework"
    - "colorful triangle counting and a mapreduce implementation"

Connected through hyperedge HE-7 (induced by source paper "general purpose join algorithms for listing triangles in large graphs"):
  with members:
    - "size bounds and query plans for relational joins"

Connected through hyperedge HE-8 (induced by source paper "subgraph enumeration in massive graphs"):
  with members:
    - "the input output complexity of triangle enumeration"
    - "counting small cliques in mapreduce"
    - "worst case optimal join algorithms"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"
    - "external memory network analysis algorithms for naturally sparse 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.DC(Distributed, Parallel, and Cluster Computing)