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 "gossip vs markov chains and randomness efficient rumor spreading"):
  Abstract: We study gossip algorithms for the rumor spreading problem which asks one node to deliver a rumor to all nodes in an unknown network. We present the first protocol for any expander graph $G$ with $n$ nodes such that, the protocol informs every node in $O(\log n)$ rounds with high probability, and uses $\tilde{O}(\log n)$ random bits in total. The runtime of our protocol is tight, and the randomness requirement of $\tilde{O}(\log n)$ random bits almost matches the lower bound of $\Omega(\log n)$ random bits for dense graphs. We further show that, for many graph families, polylogarithmic number of random bits in total suffice to spread the rumor in $O(\mathrm{poly}\log n)$ rounds. These results together give us an almost complete understanding of the randomness requirement of this fundamental gossip process. #R##N#Our analysis relies on unexpectedly tight connections among gossip processes, Markov chains, and branching programs. First, we establish a connection between rumor spreading processes and Markov chains, which is used to approximate the rumor spreading time by the mixing time of Markov chains. Second, we show a reduction from rumor spreading processes to branching programs, and this reduction provides a general framework to derandomize gossip processes. In addition to designing rumor spreading protocols, these novel techniques may have applications in studying parallel and multiple random walks, and randomness complexity of distributed algorithms.

Includes members:
  Member "rumor spreading in random evolving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the radius of nonsplit graphs and information dissemination in dynamic networks")
  Member "tight bounds for rumor spreading with vertex expansion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the capacity of smartphone peer to peer networks")
  Member "quasirandom load balancing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrepancy analysis of a new randomized diffusion algorithm for weighted round matrices")
  Member "tight bounds for randomized load balancing on arbitrary network topologies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel balanced allocations the heavily loaded case")
  Member "simple fast and deterministic gossip and rumor spreading":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "network coding gaps for completion times of multiple unicasts")
  Member "better pseudorandom generators from milder pseudorandom restrictions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exponentially faster shortest paths in the congested clique")
  Member "global computation in a poorly connected world fast rumor spreading with no dependence on conductance":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "message reduction in the local model is a free lunch")
  Member "space efficient local computation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "seeing far vs seeing wide volume complexity of local graph problems")

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)