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 "distributed random walks"):
  Abstract: Performing random walks in networks is a fundamental primitive that has found applications in many areas of computer science, including distributed computing. In this paper, we focus on the problem of sampling random walks efficiently in a distributed network and its applications. Given bandwidth constraints, the goal is to minimize the number of rounds required to obtain random walk samples. #R##N#All previous algorithms that compute a random walk sample of length $\ell$ as a subroutine always do so naively, i.e., in $O(\ell)$ rounds. The main contribution of this paper is a fast distributed algorithm for performing random walks. We present a sublinear time distributed algorithm for performing random walks whose time complexity is sublinear in the length of the walk. Our algorithm performs a random walk of length $\ell$ in $\tilde{O}(\sqrt{\ell D})$ rounds ($\tilde{O}$ hides $\polylog{n}$ factors where $n$ is the number of nodes in the network) with high probability on an undirected network, where $D$ is the diameter of the network. For small diameter graphs, this is a significant improvement over the naive $O(\ell)$ bound. Furthermore, our algorithm is optimal within a poly-logarithmic factor as there exists a matching lower bound [Nanongkai et al. PODC 2011]. We further extend our algorithms to efficiently perform $k$ independent random walks in $\tilde{O}(\sqrt{k\ell D} + k)$ rounds. We also show that our algorithm can be applied to speedup the more general Metropolis-Hastings sampling. #R##N#Our random walk algorithms can be used to speed up distributed algorithms in applications that use random walks as a subroutine, such as computing a random spanning tree and estimating mixing time and related parameters. Our algorithm is fully decentralized and can serve as a building block in the design of topologically-aware networks.

Includes members:
  Member "near optimal random walk sampling in distributed networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "plurality consensus in the gossip model")
  Member "fast distributed computation in dynamic networks via random walks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "information spreading in dynamic networks under oblivious adversaries")
  Member "distributed verification and hardness of distributed approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond alice and bob improved inapproximability for maximum independent set in congest")
  Member "faster generation of random spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")
  Member "efficient distributed random walks with applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the communication cost of information spreading in dynamic networks")
  Member "expanders via random spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random spanning trees for expanders sparsifiers and virtual network security")
  Member "lower bounds for distributed markov chain problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "efficient distributed random walks with applications")
  Member "search in power law networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a general centrality framework based on node navigability")

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)