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: "broadcasting in noisy radio networks"
  Abstract: The widely-studied radio network model [Chlamtac and Kutten, 1985] is a graph-based description that captures the inherent impact of collisions in wireless communication. In this model, the strong assumption is made that node v receives a message from a neighbor if and only if exactly one of its neighbors broadcasts. We relax this assumption by introducing a new noisy radio network model in which random faults occur at senders or receivers. Specifically, for a constant noise parameter p ∈ [0,1), either every sender has probability p of transmitting noise or every receiver of a single transmission in its neighborhood has probability p of receiving noise.   We first study single-message broadcast algorithms in noisy radio networks and show that the Decay algorithm [Bar-Yehuda et al., 1992] remains robust in the noisy model while the diameter-linear algorithm of Gasieniec et al., 2007 does not. We give a modified version of the algorithm of Gasieniec et al., 2007 that is robust to sender and receiver faults, and extend both this modified algorithm and the Decay algorithm to robust multi-message broadcast algorithms, broadcasting Ω(1/log n log log n) and Ω(1/log n) messages per round, respectively.   We next investigate the extent to which (network) coding improves throughput in noisy radio networks. In particular, we study the coding cap -- the ratio of the throughput of coding to that of routing -- in noisy radio networks. We address the previously perplexing result of Alon et al. 2014 that worst case coding throughput is no better than worst case routing throughput up to constants: we show that the worst case throughput performance of coding is, in fact, superior to that of routing -- by a Θ(log(n)) gap -- provided receiver faults are introduced. However, we show that sender faults have little effect on throughput. In particular, we show that any coding or routing scheme for the noiseless setting can be transformed to be robust to sender faults with only a constant throughput overhead. These transformations imply that the results of Alon et al., 2014 carry over to noisy radio networks with sender faults as well. As a result, if sender faults are introduced then there exist topologies for which there is a Θ(log log n) gap, but the worst case throughput across all topologies is Θ(1/log n) for both coding and routing.

Connected through hyperedge HE-1 (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol"):
  with members:
    - "almost logarithmic time space optimal leader election in population protocols"
    - "fast space optimal leader election in population protocols"
    - "erasure correction for noisy radio networks"
    - "limits for rumor spreading in stochastic populations"
    - "ignore or comply on breaking symmetry in consensus"
    - "space optimal majority in population protocols"
    - "rapid asynchronous plurality consensus"
    - "efficient k party voting with two choices"

Connected through hyperedge HE-2 (induced by source paper "noisy beeping networks"):
  with members:
    - "leader election in multi hop radio networks"
    - "efficient multiparty interactive coding for insertions deletions and substitutions"
    - "erasure correction for noisy radio networks"
    - "naming a channel with beeps"
    - "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring"
    - "design patterns in beeping algorithms examples emulation and analysis"
    - "communicating with beeps"
    - "the adversarial noise threshold for distributed protocols"

Connected through hyperedge HE-3 (induced by source paper "erasure correction for noisy radio networks"):
  with members:
    - "multi message broadcast with abstract mac layers and unreliable links"
    - "interactive channel capacity revisited"
    - "randomized broadcast in radio networks with collision detection"
    - "bounds on contention management in radio networks"

Connected through hyperedge HE-4 (induced by source paper "latency optimal broadcasting in noisy wireless mesh networks"):
  with members:
    - "randomized broadcast in radio networks with collision detection"
    - "broadcast throughput in radio networks routing vs network coding"
    - "fast structuring of radio networks large for multi message communications"
    - "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"
    - "exploiting spontaneous transmissions for broadcasting and leader election in radio networks"
    - "the computational power of beeps"
    - "optimal leader election in multi hop radio networks"
    - "radio network lower bounds made easy"
    - "near optimal leader election in multi hop radio networks"
    - "deploying wireless networks with beeps"

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)