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 "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"):
  Abstract: Distributed computing models typically assume reliable communication between processors. While such assumptions often hold for engineered networks, e.g., due to underlying error correction protocols, their relevance to biological systems, wherein messages are often distorted before reaching their destination, is quite limited. In this study we aim at bridging this gap by rigorously analyzing a model of communication in large anonymous populations composed of simple agents which interact through short and highly unreliable messages. We focus on the rumor-spreading problem and the majority-consensus problem, two fundamental tasks in distributed computing, and initiate their study under communication noise. Our model for communication is extremely weak and follows the push gossip communication paradigm: In each synchronous round each agent that wishes to send information delivers a message to a random anonymous agent. This communication is further restricted to contain only one bit (essentially representing an opinion). Lastly, the system is assumed to be so noisy that the bit in each message sent is flipped independently with probability 1/2-e, for some small Ae >0.   Even in this severely restricted, stochastic and noisy setting we give natural protocols that solve the noisy rumor-spreading and the noisy majority-consensus problems efficiently. Our protocols run in O(log n / e2) rounds and use O(n log n / e2) messages/bits in total, where n is the number of agents. These bounds are asymptotically optimal and, in fact, are as fast and message efficient as if each agent would have been simultaneously informed directly by the source. Our efficient, robust, and simple algorithms suggest balancing between silence and transmission, synchronization, and majority-based decisions as important ingredients towards understanding collective communication schemes in anonymous and noisy populations.

Includes members:
  Member "the power of two choices in distributed voting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed voting in beep model")
  Member "optimal gossip with direct addressing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "consensus needs broadcast in noiseless models but can be exponentially easier in the presence of noise")
  Member "simple dynamics for plurality consensus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed voting in beep model")
  Member "memory lower bounds for randomized collaborative search and applications to biology":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the ants problem")
  Member "collaborative search on the plane without communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistically faulty searching on a half line")
  Member "beeping a maximal independent set":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed voting in beep model")
  Member "simple fast and deterministic gossip and rumor spreading":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "network coding gaps for completion times of multiple unicasts")
  Member "global computation in a poorly connected world fast rumor spreading with no dependence on conductance":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "message reduction in the local model is a free lunch")

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)