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: "counting small cliques in mapreduce"
  Abstract: We present exact and approximate MapReduce estimators for the number of cliques of size k in an undirected graph, for any small constant k >= 3. Besides theoretically analyzing our algorithms in the computational model for MapReduce introduced by Karloff, Suri, and Vassilvitskii, we present the results of extensive computational experiments on the Amazon EC2 platform. Our experiments show the practical effectiveness of our algorithms even on clusters of small/medium size, and suggest their scalability to larger clusters.

Connected through hyperedge HE-1 (induced by source paper "on data skewness stragglers and mapreduce progress indicators"):
  with members:
    - "clique counting in mapreduce algorithms and experiments"
    - "experimental evaluation of multi round matrix multiplication on mapreduce"
    - "perfxplain debugging mapreduce job performance"

Connected through hyperedge HE-2 (induced by source paper "a novel approach to finding near cliques the triangle densest subgraph problem"):
  with members:
    - "mathematical and algorithmic analysis of network and biological data"
    - "a second look at counting triangles in graph streams revised"
    - "decompositions of triangle dense graphs"
    - "parallel peeling algorithms"
    - "how hard is counting triangles in the streaming model"
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "densest subgraph in streaming and mapreduce"
    - "efficient triangle counting in large graphs via degree based vertex partitioning"

Connected through hyperedge HE-3 (induced by source paper "subgraph enumeration in massive graphs"):
  with members:
    - "the input output complexity of triangle enumeration"
    - "enumerating subgraph instances using map reduce"
    - "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)