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: "radio network lower bounds made easy"
  Abstract: Theoreticians have studied distributed algorithms in the radio network model for close to three decades. A significant fraction of this work focuses on lower bounds for basic communication problems such as wake-up (symmetry breaking among an unknown set of nodes) and broadcast (message dissemination through an unknown network topology). In this paper, we introduce a new technique for proving this type of bound, based on reduction from a probabilistic hitting game, that simplifies and strengthens much of this existing work. In more detail, in this single paper we prove new expected time and high probability lower bounds for wake-up and global broadcast in single and multichannel versions of the radio network model both with and without collision detection. In doing so, we are able to reproduce results that previously spanned a half-dozen papers published over a period of twenty-five years. In addition to simplifying these existing results, our technique, in many places, also improves the state of the art: of the eight bounds we prove, four strictly strengthen the best known previous result (in terms of time complexity and/or generality of the algorithm class for which it holds), and three provide the first known non-trivial bound for the case in question. The fact that the same technique can easily generate this diverse collection of lower bounds indicates a surprising unity underlying communication tasks in the radio network model---revealing that deep down, below the specifics of the problem definition and model assumptions, communication in this setting reduces to finding efficient strategies for a simple game.

Connected through hyperedge HE-1 (induced by source paper "the energy complexity of broadcast"):
  with members:
    - "exploiting spontaneous transmissions for broadcasting and leader election in radio networks"
    - "exponential separations in the energy complexity of leader election"
    - "randomized broadcast in radio networks with collision detection"
    - "parallel graph decompositions using random shifts"
    - "near optimal leader election in multi hop radio networks"

Connected through hyperedge HE-2 (induced by source paper "communication primitives in cognitive radio networks"):
  with members:
    - "an optimal randomized broadcasting algorithm in radio networks with collision detection"
    - "robust neighbor discovery in multi hop multi channel heterogeneous wireless networks"
    - "randomized broadcast in radio networks with collision detection"
    - "deterministic blind rendezvous in cognitive radio networks"

Connected through hyperedge HE-3 (induced by source paper "data dissemination in unified dynamic wireless networks"):
  with members:
    - "leveraging multiple channels in ad hoc networks"
    - "distributed bare bones communication in wireless networks"
    - "a local broadcast layer for the sinr network model"
    - "distributed communication in bare bones wireless networks"
    - "multi message broadcast with abstract mac layers and unreliable links"
    - "randomized broadcast in radio networks with collision detection"
    - "beyond geometry towards fully realistic wireless models"
    - "on the impact of geometry on ad hoc communication in wireless networks"

Connected through hyperedge HE-4 (induced by source paper "the cost of global broadcast in dynamic radio networks"):
  with members:
    - "multi message broadcast in dynamic radio networks"
    - "dynamic multiple message broadcast bounding throughput in the affectance model"
    - "multi message broadcast with abstract mac layers and unreliable links"
    - "on the impact of geometry on ad hoc communication in wireless networks"
    - "distributed connectivity decomposition"
    - "a new perspective on vertex connectivity"
    - "analyzing network coding gossip made easy"

Connected through hyperedge HE-5 (induced by source paper "exponential separations in the energy complexity of leader election"):
  with members:
    - "the energy complexity of broadcast"
    - "broadcasting in noisy radio networks"
    - "exploiting spontaneous transmissions for broadcasting and leader election in radio networks"
    - "the computational power of beeps"
    - "optimal leader election in multi hop radio networks"
    - "near optimal leader election in multi hop radio networks"
    - "deploying wireless networks with beeps"

Connected through hyperedge HE-6 (induced by source paper "slow links fast links and the cost of gossip"):
  with members:
    - "rumor spreading with bounded in degree"
    - "optimal gossip with direct addressing"
    - "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-7 (induced by source paper "multi message broadcast in dynamic radio networks"):
  with members:
    - "the cost of global broadcast in dynamic radio networks"
    - "dynamic multiple message broadcast bounding throughput in the affectance model"
    - "multi message broadcast with abstract mac layers and unreliable links"
    - "time varying graphs and dynamic networks"
    - "analyzing network coding gossip made easy"

Connected through hyperedge HE-8 (induced by source paper "gossiping with latencies"):
  with members:
    - "rumor spreading with bounded in degree"
    - "optimal gossip with direct addressing"
    - "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"

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)