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 "global information sharing under network dynamics"):
  Abstract: We study how to spread $k$ tokens of information to every node on an $n$-node dynamic network, the edges of which are changing at each round. This basic {\em gossip problem} can be completed in $O(n + k)$ rounds in any static network, and determining its complexity in dynamic networks is central to understanding the algorithmic limits and capabilities of various dynamic network models. Our focus is on token-forwarding algorithms, which do not manipulate tokens in any way other than storing, copying and forwarding them. #R##N#We first consider the {\em strongly adaptive} adversary model where in each round, each node first chooses a token to broadcast to all its neighbors (without knowing who they are), and then an adversary chooses an arbitrary connected communication network for that round with the knowledge of the tokens chosen by each node. We show that $\Omega(nk/\log n + n)$ rounds are needed for any randomized (centralized or distributed) token-forwarding algorithm to disseminate the $k$ tokens, thus resolving an open problem raised in~\cite{kuhn+lo:dynamic}. The bound applies to a wide class of initial token distributions, including those in which each token is held by exactly one node and {\em well-mixed} ones in which each node has each token independently with a constant probability. #R##N#We also show several upper bounds in varying models.

Includes members:
  Member "information spreading in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "lower bounds on information dissemination in dynamic networks")
  Member "information spreading in dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relative hausdorff distance for network analysis")
  Member "faster information dissemination in dynamic networks via network coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "local distributed algorithms in highly dynamic networks")
  Member "order optimal information spreading using algebraic gossip":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable byzantine reliable broadcast extended version")
  Member "time varying graphs and dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "edge disjoint branchings in temporal graphs")
  Member "analyzing network coding gossip made easy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "synchronous transmissions in low power wireless a survey of communication protocols and network services")
  Member "optimal gossip based aggregate computation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rumor spreading with bounded in degree")

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)