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: "how to discreetly spread a rumor in a crowd"
  Abstract: In this paper, we study PUSH-PULL style rumor spreading algorithms in the mobile telephone model, a variant of the classical telephone model in which each node can participate in at most one connection per round; i.e., you can no longer have multiple nodes pull information from the same source in a single round. Our model also includes two new parameterized generalizations: (1) the network topology can undergo a bounded rate of change (for a parameterized rate that spans from no changes to changes in every round); and (2) in each round, each node can advertise a bounded amount of information to all of its neighbors before connection decisions are made (for a parameterized number of bits that spans from no advertisement to large advertisements). We prove that in the mobile telephone model with no advertisements and no topology changes, PUSH-PULL style algorithms perform poorly with respect to a graph's vertex expansion and graph conductance as compared to the known tight results in the classical telephone model. We then prove, however, that if nodes are allowed to advertise a single bit in each round, a natural variation of PUSH-PULL terminates in time that matches (within logarithmic factors) this strategy's performance in the classical telephone model---even in the presence of frequent topology changes. We also analyze how the performance of this algorithm degrades as the rate of change increases toward the maximum possible amount. We argue that our model matches well the properties of emerging peer-to-peer communication standards for mobile devices, and that our efficient PUSH-PULL variation that leverages small advertisements and adapts well to topology changes is a good choice for rumor spreading in this increasingly important setting.

Connected through hyperedge HE-1 (induced by source paper "who started this rumor quantifying the natural differential privacy guarantees of gossip protocols"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "stochastic gradient push for distributed deep learning"
    - "distributed differential privacy via shuffling"
    - "personalized and private peer to peer machine learning"
    - "decentralized collaborative learning of personalized models over networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "hiding the rumor source"

Connected through hyperedge HE-2 (induced by source paper "the capacity of smartphone peer to peer networks"):
  with members:
    - "random gossip processes in smartphone peer to peer networks"
    - "rumor spreading with bounded in degree"
    - "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-3 (induced by source paper "random gossip processes in smartphone peer to peer networks"):
  with members:
    - "gossip in a smartphone peer to peer network"
    - "energy efficient resource allocation for device to device underlay communication"
    - "tight bounds for rumor spreading with vertex expansion"

Connected through hyperedge HE-4 (induced by source paper "gossip in a smartphone peer to peer network"):
  with members:
    - "rumor spreading with bounded in degree"
    - "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.DS(Data Structures and Algorithms)