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 "communicating with beeps"):
  Abstract: The beep model is a very weak communications model in which devices in a network can communicate only via beeps and silence. As a result of its weak assumptions, it has broad applicability to many different implementations of communications networks. This comes at the cost of a restrictive environment for algorithm design. #R##N#Despite being only recently introduced, the beep model has received considerable attention, in part due to its relationship with other communication models such as that of ad-hoc radio networks. However, there has been no definitive published result for several fundamental tasks in the model. We aim to rectify this with our paper. #R##N#We present algorithms for the tasks of broadcast, gossiping, and multi-broadcast. Our $O(D+\log M)$-time algorithm for broadcasting is a simple formalization of a concept known as beep waves, and is asymptotically optimal. We give an $O(n \log L)$-time depth-first search procedure, and show how this can be used as the basis for an $O(n \log LM)$-time gossiping algorithm. Finally, we present almost optimal algorithms for the more general problem of multi-broadcast. When message provenance is required, we give an $O(k \log \frac{LM}{k}+D \log L)$-time algorithm and a corresponding $\Omega(k \log \frac{LM}{k}+D)$ lower bound. When provenance is not required, we give an algorithm taking $O(k \log \frac {M}{k}+D \log L)$ time when $M>k$ and $O(M+D \log L)$ otherwise, and a corresponding lower bound of $\Omega(k \log \frac{M}{k}+D)$ when $M>k$ and $\Omega(M+D)$ otherwise. #R##N#Our algorithms are all explicit, deterministic, and practical, and give efficient means of communication while making arguably the minimum possible assumptions about the network.

Includes members:
  Member "leader election in multi hop radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noisy beeping networks")
  Member "naming a channel with beeps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed voting in beep model")
  Member "asynchronous broadcasting with bivalent beeps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed voting in beep model")
  Member "the computational power of beeps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed voting in beep model")
  Member "global synchronization and consensus using beeps in a fault prone mac":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed voting in beep model")
  Member "deterministic broadcasting and gossiping with beeps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "competitive broadcast against adaptive adversary in multi channel radio networks")
  Member "beeping a maximal independent set":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed voting in beep model")

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)