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: "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.

Connected through hyperedge HE-1 (induced by source paper "efficient distributed community detection in the stochastic block model"):
  with members:
    - "large scale distributed algorithms for facility location with outliers"
    - "local mixing time distributed computation and applications"
    - "average whenever you meet opportunistic protocols for community detection"
    - "on the distributed complexity of large scale graph computations"
    - "find your place simple distributed algorithms for community detection"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "partitioning well clustered graphs spectral clustering works"

Connected through hyperedge HE-2 (induced by source paper "tight bounds for distributed graph computations"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "worst case optimal algorithms for parallel query processing"
    - "algebraic methods in the congested clique"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "fast distributed pagerank computation"
    - "almost optimal distributed algorithms for large scale graph problems"
    - "the input output complexity of triangle enumeration"
    - "why do simple algorithms for triangle enumeration work in the real world"

Connected through hyperedge HE-3 (induced by source paper "on the distributed complexity of large scale graph computations"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "triangle finding and listing in congest networks"
    - "tight bounds for distributed graph computations"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "algebraic methods in the congested clique"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "fast distributed pagerank computation"
    - "distributed computation of large scale graph problems"

Connected through hyperedge HE-4 (induced by source paper "fast distributed algorithms for connectivity and mst in large graphs"):
  with members:
    - "tight bounds for distributed graph computations"
    - "on the distributed complexity of large scale graph computations"
    - "a time and message optimal distributed algorithm for minimum spanning trees"
    - "super fast mst algorithms in the congested clique using o m messages"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "algebraic methods in the congested clique"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"
    - "computing exact distances in the congested clique"

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)