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 "rumor spreading in random evolving graphs"):
  Abstract: Randomized gossip is one of the most popular way of disseminating information in large scale networks. This method is appreciated for its simplicity, robustness, and efficiency. In the "push" protocol, every informed node selects, at every time step (a.k.a. round), one of its neighboring node uniformly at random and forwards the information to this node. This protocol is known to complete information spreading in $O(\log n)$ time steps with high probability (w.h.p.) in several families of $n$-node "static" networks. The Push protocol has also been empirically shown to perform well in practice, and, specifically, to be robust against dynamic topological changes. #R##N#In this paper, we aim at analyzing the Push protocol in "dynamic" networks. We consider the "edge-Markovian" evolving graph model which captures natural temporal dependencies between the structure of the network at time $t$, and the one at time $t+1$. Precisely, a non-edge appears with probability $p$, while an existing edge dies with probability $q$. In order to fit with real-world traces, we mostly concentrate our study on the case where $p=\Omega(1/n)$ and $q$ is constant. We prove that, in this realistic scenario, the Push protocol does perform well, completing information spreading in $O(\log n)$ time steps w.h.p. Note that this performance holds even when the network is, w.h.p., disconnected at every time step (e.g., when $p << (\log n) / n$). Our result provides the first formal argument demonstrating the robustness of the Push protocol against network changes. We also address other ranges of parameters $p$ and $q$ (e.g., $p+q=1$ with arbitrary $p$ and $q$, and $p=1/n$ with arbitrary $q$). Although they do not precisely fit with the measures performed on real-world traces, they can be of independent interest for other settings. The results in these cases confirm the positive impact of dynamism.

Includes members:
  Member "information spreading in stationary markovian evolving graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "how fast can we reach a target vertex in stochastic temporal graphs")
  Member "performance of opportunistic epidemic routing on edge markovian dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the performance analysis of epidemic routing in non sparse delay tolerant networks")
  Member "strong robustness of randomized rumor spreading protocols":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal broadcasting in networks with faulty nodes")
  Member "information propagation speed in mobile and delay tolerant networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "diffusion on dynamic contact networks with indirect transmission links")

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.DM(Discrete Mathematics)