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 "randomized communication without network knowledge"):
  Abstract: Radio networks are a long-studied model for distributed system of devices which communicate wirelessly. When these devices are mobile or have limited capabilities, the system is often best modeled by the ad-hoc variant, in which the devices do not know the structure of the network. A large body of work has been devoted to designing algorithms for the ad-hoc model, particularly for fundamental communications tasks such as broadcasting. Most of these algorithms, however, assume that devices have some network knowledge (usually bounds on the number of nodes in the network $n$, and the diameter $D$), which may not always be realistic in systems with weak devices or gradual deployment. Very little is known about what can be done when this information is not available. #R##N#This is the issue we address in this work, by presenting the first \emph{randomized} broadcasting algorithms for \emph{blind} networks in which nodes have no prior knowledge whatsoever. We demonstrate that lack of parameter knowledge can be overcome at only a small increase in running time. Specifically, we show that in networks without collision detection, broadcast can be achieved in $O(D\log\frac nD\log^2\log\frac nD + \log^2 n)$ time, almost reaching the $\Omega(D\log\frac nD + \log^2 n)$ lower bound. We also give an algorithm for directed networks with collision detection, which requires only $O(D\log\frac nD\log\log\log\frac nD + \log^2 n)$ time.

Includes members:
  Member "deterministic blind radio networks":
    also connected through other hyperedges:
      (none)
  Member "deterministic communication in radio networks":
    also connected through other hyperedges:
      (none)
  Member "exploiting spontaneous transmissions for broadcasting and leader election in radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "competitive broadcast against adaptive adversary in multi channel radio networks")
  Member "communicating with beeps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed voting in beep model")
  Member "randomized broadcast in radio networks with collision detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "competitive broadcast against adaptive adversary in multi channel radio networks")

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.DC(Distributed, Parallel, and Cluster Computing)