human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "erasure correction for noisy radio networks"):
  Abstract: The radio network model is a well-studied abstraction for modeling wireless multi-hop networks. However, radio networks make the strong assumption that messages are delivered deterministically. The recently introduced noisy radio network model relaxes this assumption by dropping messages independently at random. #R##N#In this work we quantify the relative computational power of noisy radio networks and classic radio networks. In particular, given a protocol for a fixed radio network we show how to reliably simulate this protocol if noise is introduced with a multiplicative cost of $\mathrm{poly}(\log \Delta, \log \log n)$ rounds. For this result we make the simplifying assumption that the simulated protocol is $\textit{static}$. Moreover, we demonstrate that, even if the simulated protocol is not static, it can be simulated with a multiplicative $O(\Delta \log \Delta)$ cost in the number of rounds. Hence, our results show that protocols on constant-degree networks can be made robust to random noise with constant multiplicative overhead. Lastly, we argue that simulations with a multiplicative overhead of $o(\log \Delta)$ are unlikely to exist by proving that an $\Omega(\log \Delta)$ multiplicative round overhead is necessary under certain natural assumptions.

Includes members:
  Member "broadcasting in noisy radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol")
  Member "multi message broadcast with abstract mac layers and unreliable links":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the capacity of smartphone peer to peer networks")
  Member "interactive channel capacity revisited":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient multiparty interactive coding for insertions deletions and substitutions")
  Member "randomized broadcast in radio networks with collision detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "competitive broadcast against adaptive adversary in multi channel radio networks")
  Member "bounds on contention management in radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross sender bit mixing coding")

Question: Please tell me which class the source paper 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)