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 with bounded in degree"
  Abstract: In the classic gossip-based model of communication for disseminating information in a network, in each time unit, every node $u$ is allowed to contact a single random neighbor $v$. If $u$ knows the data (rumor) to be disseminated, it disperses it to $v$ (known as PUSH) and if it does not, it requests it from $v$ (known as PULL). While in the classic gossip model, each node is only allowed to contact a single neighbor in each time unit, each node can possibly be contacted by many neighboring nodes. #R##N#In the present paper, we consider a restricted model where at each node only one incoming request can be served. As long as only a single piece of information needs to be disseminated, this does not make a difference for push requests. It however has a significant effect on pull requests. In the paper, we therefore concentrate on this weaker pull version, which we call 'restricted pull'. #R##N#We distinguish two versions of the restricted pull protocol depending on whether the request to be served among a set of pull requests at a given node is chosen adversarially or uniformly at random. As a first result, we prove an exponential separation between the two variants. We show that there are instances where if an adversary picks the request to be served, the restricted pull protocol requires a polynomial number of rounds whereas if the winning request is chosen uniformly at random, the restricted pull protocol only requires a polylogarithmic number of rounds to inform the whole network. Further, as the main technical contribution, we show that if the request to be served is chosen randomly, the slowdown of using restricted pull versus using the classic pull protocol can w.h.p. be upper bounded by $O(\Delta / \delta \log n)$, where $\Delta$ and $\delta$ are the largest and smallest degree of the network.

Connected through hyperedge HE-1 (induced by source paper "the capacity of smartphone peer to peer networks"):
  with members:
    - "random gossip processes in smartphone peer to peer networks"
    - "how to discreetly spread a rumor in a crowd"
    - "multi message broadcast with abstract mac layers and unreliable links"
    - "tight bounds for rumor spreading with vertex expansion"
    - "faster approximation schemes for fractional multicommodity flow problems via dynamic graph algorithms"

Connected through hyperedge HE-2 (induced by source paper "gossip in a smartphone peer to peer network"):
  with members:
    - "how to discreetly spread a rumor in a crowd"
    - "tight bounds for rumor spreading with vertex expansion"

Connected through hyperedge HE-3 (induced by source paper "pull and push pull in random evolving graphs"):
  with members:
    - "tight bounds for rumor spreading with vertex expansion"

Connected through hyperedge HE-4 (induced by source paper "slow links fast links and the cost of gossip"):
  with members:
    - "optimal gossip with direct addressing"
    - "radio network lower bounds made easy"
    - "simple fast and deterministic gossip and rumor spreading"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"
    - "the impact of mobility on gossip algorithms"

Connected through hyperedge HE-5 (induced by source paper "gossiping with latencies"):
  with members:
    - "optimal gossip with direct addressing"
    - "radio network lower bounds made easy"
    - "simple fast and deterministic gossip and rumor spreading"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"
    - "the impact of mobility on gossip algorithms"

Connected through hyperedge HE-6 (induced by source paper "how to discreetly spread a rumor in a crowd"):
  with members:
    - "tight bounds for rumor spreading with vertex expansion"

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.DC(Distributed, Parallel, and Cluster Computing)