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: "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.

Connected through hyperedge HE-1 (induced by source paper "walking randomly massively and efficiently"):
  with members:
    - "exponentially faster massively parallel maximal matching"
    - "simulating random walks on graphs in the streaming model"
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "testing graph clusterability algorithms and lower bounds"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "weighted matchings via unweighted augmentations"
    - "round compression for parallel graph algorithms in strongly sublinear space"

Connected through hyperedge HE-2 (induced by source paper "distributed mst a smoothed analysis"):
  with members:
    - "fast approximate shortest paths in the congested clique"
    - "fast and efficient distributed computation of hamiltonian cycles in random graphs"
    - "congested clique algorithms for graph spanners"
    - "new distributed algorithms in almost mixing time via transformations from parallel algorithms"
    - "distributed symmetry breaking algorithms for congested cliques"
    - "a simple deterministic distributed mst algorithm with near optimal time and message complexities"
    - "towards a complexity theory for the congested clique"
    - "a time and message optimal distributed algorithm for minimum spanning trees"

Connected through hyperedge HE-3 (induced by source paper "massively parallel algorithms for finding well connected components in sparse graphs"):
  with members:
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "brief announcement semi mapreduce meets congested clique"
    - "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine"
    - "the complexity of delta 1 coloring incongested clique massively parallel computation and centralized local computation"
    - "mis in the congested clique model in o log log δ rounds"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "mst in o 1 rounds of the congested clique"

Connected through hyperedge HE-4 (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"
    - "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"
    - "analyzing network coding gossip made easy"

Connected through hyperedge HE-5 (induced by source paper "local mixing time distributed computation and applications"):
  with members:
    - "distributed computation of mixing time"
    - "fast distributed pagerank computation"
    - "metastability of logit dynamics for coordination games"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-6 (induced by source paper "what can be sampled locally"):
  with members:
    - "on local distributed sampling and counting"
    - "distributed degree splitting edge coloring and orientations"
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "bis hardness for 2 spin systems on bipartite bounded degree graphs in the tree non uniqueness region"
    - "uniform sampling through the lov asz local lemma"
    - "convergence of mcmc and loopy bp in the tree uniqueness region for the hard core model"
    - "an exponential separation between randomized and deterministic complexity in the local model"
    - "inapproximability of the partition function for the antiferromagnetic ising and hard core models"

Connected through hyperedge HE-7 (induced by source paper "a time and message optimal distributed algorithm for minimum spanning trees"):
  with members:
    - "time message trade offs in distributed algorithms"
    - "round and message optimal distributed graph algorithms"
    - "symmetry breaking in the congest model time and message efficient algorithms for ruling sets"
    - "a simple deterministic distributed mst algorithm with near optimal time and message complexities"
    - "low congestion shortcuts without embedding"
    - "a deterministic almost tight distributed algorithm for approximating single source shortest paths"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"
    - "distributed symmetry breaking in hypergraphs"

Connected through hyperedge HE-8 (induced by source paper "fast distributed algorithms for testing graph properties"):
  with members:
    - "triangle finding and listing in congest networks"
    - "survey of distributed decision"
    - "algebraic methods in the congested clique"
    - "non local probes do not help with graph problems"
    - "large cuts with local algorithms on triangle free graphs"
    - "tri tri again finding triangles and small subgraphs in a distributed setting"
    - "distributed verification and hardness of distributed approximation"
    - "distributed discovery of large near cliques"

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)