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 maximal bicliques from a large graph using mapreduce"
  Abstract: We consider the enumeration of maximal bipartite cliques (bicliques) from a large graph, a task central to many practical data mining problems in social network analysis and bioinformatics. We present novel parallel algorithms for the MapReduce platform, and an experimental evaluation using Hadoop MapReduce. Our algorithm is based on clustering the input graph into smaller sized subgraphs, followed by processing different subgraphs in parallel. Our algorithm uses two ideas that enable it to scale to large graphs: (1) the redundancy in work between different subgraph explorations is minimized through a careful pruning of the search space, and (2) the load on different reducers is balanced through the use of an appropriate total order among the vertices. Our evaluation shows that the algorithm scales to large graphs with millions of edges and tens of mil- lions of maximal bicliques. To our knowledge, this is the first work on maximal biclique enumeration for graphs of this scale.

Connected through hyperedge HE-1 (induced by source paper "shared memory parallel maximal clique enumeration from static and dynamic graphs"):
  with members:
    - "enumerating top k quasi cliques"
    - "efficiently enumerating all maximal cliques with bit parallelism"
    - "local algorithms for hierarchical dense subgraph discovery"
    - "shared memory graph truss decomposition"
    - "incremental maintenance of maximal cliques in a dynamic graph"
    - "listing all maximal cliques in large sparse real world graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-2 (induced by source paper "enumerating top k quasi cliques"):
  with members:
    - "change sensitive algorithms for maintaining maximal cliques in a dynamic graph"
    - "a novel approach to finding near cliques the triangle densest subgraph problem"
    - "mining attribute structure correlated patterns in large attributed graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"
    - "an o m algorithm for cores decomposition of networks"

Connected through hyperedge HE-3 (induced by source paper "shared memory parallel maximal clique enumeration"):
  with members:
    - "local algorithms for hierarchical dense subgraph discovery"
    - "shared memory graph truss decomposition"
    - "change sensitive algorithms for maintaining maximal cliques in a dynamic graph"
    - "listing all maximal cliques in large sparse real world graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-4 (induced by source paper "a change sensitive algorithm for maintaining maximal bicliques in a dynamic bipartite graph"):
  with members:
    - "incremental maintenance of maximal cliques in a dynamic graph"

Connected through hyperedge HE-5 (induced by source paper "incremental maintenance of maximal cliques in a dynamic graph"):
  with members:
    - "a change sensitive algorithm for maintaining maximal bicliques in a dynamic bipartite graph"
    - "densest subgraph in dynamic graph streams"
    - "densest subgraph in streaming and mapreduce"
    - "faster deterministic fully dynamic graph connectivity"
    - "listing all maximal cliques in large sparse real world graphs"
    - "listing all maximal cliques in sparse graphs in near optimal time"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-6 (induced by source paper "peeling bipartite networks for dense subgraph discovery"):
  with members:
    - "higher order organization of complex networks"
    - "scalable motif aware graph clustering"
    - "measuring and modeling bipartite graphs with community structure"
    - "generalized two mode cores"
    - "finding the hierarchy of dense subgraphs using nucleus decompositions"
    - "an o m algorithm for cores decomposition of networks"

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)