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: "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.

Connected through hyperedge HE-1 (induced by source paper "on the radius of nonsplit graphs and information dissemination in dynamic networks"):
  with members:
    - "consensus in rooted dynamic networks with short lived stability"
    - "approximate consensus in highly dynamic networks the role of averaging algorithms"

Connected through hyperedge HE-2 (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"
    - "distributed random walks"
    - "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-3 (induced by source paper "plurality consensus in the gossip model"):
  with members:
    - "the power of two choices in distributed voting"
    - "determining majority in networks with local interactions and very small local memory"
    - "distributed community detection in dynamic graphs"
    - "simple dynamics for plurality consensus"
    - "timing in chemical reaction networks"
    - "consensus on the initial global majority by local majority polling for a class of sparse graphs"
    - "near optimal random walk sampling in distributed networks"
    - "discovery through gossip"

Connected through hyperedge HE-4 (induced by source paper "testing temporal connectivity in sparse dynamic graphs"):
  with members:
    - "characterizing topological assumptions of distributed algorithms in dynamic networks"
    - "time varying graphs and dynamic networks"

Connected through hyperedge HE-5 (induced by source paper "gossip vs markov chains and randomness efficient rumor spreading"):
  with members:
    - "tight bounds for rumor spreading with vertex expansion"
    - "quasirandom load balancing"
    - "tight bounds for randomized load balancing on arbitrary network topologies"
    - "simple fast and deterministic gossip and rumor spreading"
    - "better pseudorandom generators from milder pseudorandom restrictions"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"
    - "space efficient local computation algorithms"
    - "analyzing network coding gossip made easy"

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.DM(Discrete Mathematics)