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 "near optimal clustering in the k machine model"):
  Abstract: The clustering problem, in its many variants, has numerous applications in operations research and computer science (e.g., in applications in bioinformatics, image processing, social network analysis, etc.). As sizes of data sets have grown rapidly, researchers have focused on designing algorithms for clustering problems in models of computation suited for large-scale computation such as MapReduce, Pregel, and streaming models. The $k$-machine model (Klauck et al., SODA 2015) is a simple, message-passing model for large-scale distributed graph processing. This paper considers three of the most prominent examples of clustering problems: the uncapacitated facility location problem, the $p$-median problem, and the $p$-center problem and presents $O(1)$-factor approximation algorithms for these problems running in $\tilde{O}(n/k)$ rounds in the $k$-machine model. These algorithms are optimal up to polylogarithmic factors because this paper also shows $\tilde{\Omega}(n/k)$ lower bounds for obtaining polynomial-factor approximation algorithms for these problems. These are the first results for clustering problems in the $k$-machine model. #R##N#We assume that the metric provided as input for these clustering problems in only implicitly provided, as an edge-weighted graph and in a nutshell, our main technical contribution is to show that constant-factor approximation algorithms for all three clustering problems can be obtained by learning only a small portion of the input metric.

Includes members:
  Member "tight bounds for distributed graph computations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse matrix multiplication with bandwidth restricted all to all communication")
  Member "near optimal approximate shortest paths and transshipment in distributed and streaming models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polynomial pass lower bounds for graph streaming algorithms")
  Member "approximate undirected transshipment and shortest paths via gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed exact shortest paths in sublinear time")
  Member "scalable facility location for massive graphs on pregel like systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi supervised learning on graphs through reach and distance diffusion")
  Member "fast distributed algorithms for connectivity and mst in large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "all distances sketches revisited hip estimators for massive graphs analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sampling sketches for concave sublinear functions of frequencies")
  Member "distributed computation of large scale graph problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "fast clustering using mapreduce":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")

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)