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 "efficient distributed community detection in the stochastic block model"):
  Abstract: Designing effective algorithms for community detection is an important and challenging problem in {\em large-scale} graphs, studied extensively in the literature. Various solutions have been proposed, but many of them are centralized with expensive procedures (requiring full knowledge of the input graph) and have a large running time. #R##N#In this paper, we present a distributed algorithm for community detection in the {\em stochastic block model} (also called {\em planted partition model}), a widely-studied and canonical random graph model for community detection and clustering. Our algorithm called {\em CDRW(Community Detection by Random Walks)} is based on random walks, and is localized and lightweight, and easy to implement. A novel feature of the algorithm is that it uses the concept of {\em local mixing time} to identify the community around a given node. #R##N#We present a rigorous theoretical analysis that shows that the algorithm can accurately identify the communities in the stochastic block model and characterize the model parameters where the algorithm works. We also present experimental results that validate our theoretical analysis. We also analyze the performance of our distributed algorithm under the CONGEST distributed model as well as the $k$-machine model, a model for large-scale distributed computations, and show that it can be efficiently implemented.

Includes members:
  Member "large scale distributed algorithms for facility location with outliers":
    also connected through other hyperedges:
      (none)
  Member "local mixing time distributed computation and applications":
    also connected through other hyperedges:
      (none)
  Member "near optimal clustering in the k machine model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tight bounds for distributed graph computations")
  Member "average whenever you meet opportunistic protocols for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "on the distributed complexity of large scale graph computations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations")
  Member "find your place simple distributed algorithms for community detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "step by step community detection for volume regular graphs")
  Member "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "randomized spectral clustering in large scale stochastic block models")
  Member "fast distributed algorithms for connectivity and mst in large graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)