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 "on the influence of graph density on randomized gossiping"):
  Abstract: Information dissemination is a fundamental problem in parallel and distributed computing. In its simplest variant, the broadcasting problem, a message has to be spread among all nodes of a graph. A prominent communication protocol for this problem is based on the random phone call model (Karp et al., FOCS 2000). In each step, every node opens a communication channel to a randomly chosen neighbor for bi-directional communication. #R##N#Motivated by replicated databases and peer-to-peer networks, Berenbrink et al., ICALP 2010, considered the gossiping problem in the random phone call model. There, each node starts with its own message and all messages have to be disseminated to all nodes in the network. They showed that any $O(\log n)$-time algorithm in complete graphs requires $\Omega(\log n)$ message transmissions per node to complete gossiping, w.h.p, while for broadcasting the average number of transmissions per node is $O(\log\log n)$. #R##N#It is known that the $O(n\log\log n)$ bound on the number of transmissions required for randomized broadcasting in complete graphs cannot be achieved in sparse graphs even if they have best expansion and connectivity properties. In this paper, we analyze whether a similar influence of the graph density also holds w.r.t. the performance of gossiping. We study analytically and empirically the communication overhead generated by randomized gossiping in random graphs and consider simple modifications of the random phone call model in these graphs. Our results indicate that, unlike in broadcasting, there is no significant difference between the performance of randomized gossiping in complete graphs and sparse random graphs. Furthermore, our simulations indicate that by tuning the parameters of our algorithms, we can significantly reduce the communication overhead compared to the traditional push-pull approach in the graphs we consider.

Includes members:
  Member "plurality consensus in the gossip model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed voting in beep model")
  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 "tighter worst case bounds on algebraic gossip":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "order optimal information spreading using algebraic gossip")
  Member "simple fast and deterministic gossip and rumor spreading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "network coding gaps for completion times of multiple unicasts")
  Member "quasirandom rumor spreading":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lower bounds for the happy coloring problems")
  Member "cover time and broadcast time":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on fast and robust information spreading in the vertex congest model")

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.DS(Data Structures and Algorithms)