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 "design patterns in beeping algorithms examples emulation and analysis"):
  Abstract: We consider networks of processes which interact with beeps. In the basic model defined by Cornejo and Kuhn (2010), processes can choose in each round either to beep or to listen. Those who beep are unable to detect simultaneous beeps. Those who listen can only distinguish between silence and the presence of at least one beep. We refer to this model as $BL$ (beep or listen). Stronger models exist where the nodes can detect collision while they are beeping ($B_{cd}L$), listening ($BL_{cd}$), or both ($B_{cd}L_{cd}$). Beeping models are weak in essence and even simple tasks are difficult or unfeasible within. #R##N#We present a set of generic building blocks (design patterns) which seem to occur frequently in the design of beeping algorithms. They include multi-slot phases: the fact of dividing the main loop into a number of specialised slots; exclusive beeps: having a single node beep at a time in a neighbourhood (within one or two hops); adaptive probability: increasing or decreasing the probability of beeping to produce more exclusive beeps; internal (resp. peripheral) collision detection: for detecting collision while beeping (resp. listening). Based on these patterns, we provide algorithms for a number of basic problems, including colouring, 2-hop colouring, degree computation, 2-hop MIS, and collision detection (in $BL$). The patterns make it possible to formulate these algorithms in a rather concise and elegant way. Their analyses are more technical; one of them improves significantly upon that of the best known MIS algorithm by Jeavons et al. (2016). Finally, inspired by a technique from Afek et al. (2013), our last contribution is to show that any Las Vegas algorithm relying on collision detection can be transposed into a Monte Carlo algorithm without collision detection at the cost of a logarithmic slowdown, which we prove is optimal.

Includes members:
  Member "randomized communication in radio networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "packet oblivious stable routing in multi hop wireless networks")
  Member "counting in one hop beeping networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed voting in beep model")
  Member "feedback from nature simple randomised distributed algorithms for maximal independent set selection and greedy colouring":
    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 "randomized broadcast in radio networks with collision detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "competitive broadcast against adaptive adversary in multi channel radio networks")
  Member "the locality of distributed symmetry breaking":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed maximum matching verification in congest")
  Member "beeping a maximal independent set":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed voting in beep model")
  Member "feedback from nature an optimal distributed algorithm for maximal independent set selection":
    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)