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: "efficient distributed random walks with applications"
  Abstract: We focus on the problem of performing random walks efficiently in a distributed network. Given bandwidth constraints, the goal is to minimize the number of rounds required to obtain a random walk sample. We first present a fast 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 (with high probability) on an undirected network, where $D$ is the diameter of the network. This improves over the previous best algorithm that ran in $\tilde{O}(\ell^{2/3}D^{1/3})$ rounds (Das Sarma et al., PODC 2009). We further extend our algorithms to efficiently perform $k$ independent random walks in $\tilde{O}(\sqrt{k\ell D} + k)$ rounds. We then show that there is a fundamental difficulty in improving the dependence on $\ell$ any further by proving a lower bound of $\Omega(\sqrt{\frac{\ell}{\log \ell}} + D)$ under a general model of distributed random walk algorithms. Our random walk algorithms are useful in speeding up distributed algorithms for a variety of applications that use random walks as a subroutine. We present two main applications. First, we give a fast distributed algorithm for computing a random spanning tree (RST) in an arbitrary (undirected) network which runs in $\tilde{O}(\sqrt{m}D)$ rounds (with high probability; here $m$ is the number of edges). Our second application is a fast decentralized algorithm for estimating mixing time and related parameters of the underlying network. Our algorithm is fully decentralized and can serve as a building block in the design of topologically-aware networks.

Connected through hyperedge HE-1 (induced by source paper "the communication cost of information spreading in dynamic networks"):
  with members:
    - "information spreading in dynamic networks under oblivious adversaries"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"
    - "rumor spreading in random evolving graphs"
    - "distributed random walks"
    - "lower bounds on information dissemination in dynamic networks"
    - "parsimonious flooding in geometric random walks"
    - "faster information dissemination in dynamic networks via network coding"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-2 (induced by source paper "fast distributed pagerank computation"):
  with members:
    - "a note on the pagerank of undirected graphs"
    - "distributed random walks"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-3 (induced by source paper "resource location based on precomputed partial random walks in dynamic networks"):
  with members:
    - "improving resource location with locally precomputed partial random walks"
    - "search in power law networks"

Connected through hyperedge HE-4 (induced by source paper "near optimal distributed approximation of minimum weight connected dominating set"):
  with members:
    - "distributed approximation algorithms for weighted shortest paths"
    - "distributed minimum cut approximation"
    - "distributed verification and hardness of distributed approximation"

Connected through hyperedge HE-5 (induced by source paper "distributed connectivity decomposition"):
  with members:
    - "distributed approximation algorithms for weighted shortest paths"
    - "distributed minimum cut approximation"
    - "a new perspective on vertex connectivity"
    - "connected domatic packings in node capacitated graphs"
    - "lower bounds on information dissemination in dynamic networks"
    - "distributed verification and hardness of distributed approximation"
    - "minimum cuts in near linear time"

Connected through hyperedge HE-6 (induced by source paper "distributed random walks"):
  with members:
    - "near optimal random walk sampling in distributed networks"
    - "fast distributed computation in dynamic networks via random walks"
    - "distributed verification and hardness of distributed approximation"
    - "faster generation of random spanning trees"
    - "expanders via random spanning trees"
    - "lower bounds for distributed markov chain problems"
    - "search in power law networks"

Connected through hyperedge HE-7 (induced by source paper "improving resource location with locally precomputed partial random walks"):
  with members:
    - "distributed random walks"
    - "search in power law networks"

Connected through hyperedge HE-8 (induced by source paper "fast distributed computation in dynamic networks via random walks"):
  with members:
    - "information spreading in dynamic networks"
    - "information spreading in dynamic graphs"
    - "faster information dissemination in dynamic networks via network coding"
    - "time varying graphs and dynamic networks"
    - "analyzing network coding gossip made easy"

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)