human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol"):
  with members:
    - "almost logarithmic time space optimal leader election in population protocols"
    - "fast space optimal leader election in population protocols"
    - "erasure correction for noisy radio networks"
    - "limits for rumor spreading in stochastic populations"
    - "ignore or comply on breaking symmetry in consensus"
    - "broadcasting in noisy radio networks"
    - "space optimal majority in population protocols"
    - "rapid asynchronous plurality consensus"

Connected through hyperedge HE-2 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics"):
  with members:
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "phase transition of the 2 choices dynamics on core periphery networks"
    - "the ants problem"
    - "limits for rumor spreading in stochastic populations"
    - "ignore or comply on breaking symmetry in consensus"
    - "distributed graph clustering and sparsification"
    - "average whenever you meet opportunistic protocols for community detection"
    - "a tight analysis of the parallel undecided state dynamics with two colors"

Connected through hyperedge HE-3 (induced by source paper "consensus needs broadcast in noiseless models but can be exponentially easier in the presence of noise"):
  with members:
    - "ignore or comply on breaking symmetry in consensus"
    - "tight analysis for the 3 majority consensus dynamics"
    - "a tight analysis of the parallel undecided state dynamics with two colors"
    - "distributed computing with channel noise"
    - "breaking the log n barrier on rumor spreading"
    - "stabilizing consensus with many opinions"
    - "noisy rumor spreading and plurality consensus"
    - "the power of two choices in distributed voting"

Connected through hyperedge HE-4 (induced by source paper "making asynchronous distributed computations robust to noise"):
  with members:
    - "coding for interactive communication correcting insertions and deletions"
    - "maximal noise in interactive communication over erasure channels and channels with feedback"
    - "balancing communication for multi party interactive coding"
    - "interactive channel capacity revisited"
    - "the adversarial noise threshold for distributed protocols"
    - "optimal error rates for interactive coding i adaptivity and other settings"
    - "optimal error rates for interactive coding ii efficiency and list decoding"

Connected through hyperedge HE-5 (induced by source paper "limits for rumor spreading in stochastic populations"):
  with members:
    - "minimizing message size in stochastic communication patterns fast self stabilizing protocols with 3 bits"
    - "information theoretic lower bounds for distributed function computation"
    - "noisy rumor spreading and plurality consensus"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"

Connected through hyperedge HE-6 (induced by source paper "optimal gossip algorithms for exact and approximate quantile computations"):
  with members:
    - "optimal quantile approximation in streams"
    - "analyzing network coding gossip made easy"
    - "medians and beyond new aggregation techniques for sensor networks"

Connected through hyperedge HE-7 (induced by source paper "self stabilizing clock synchronization with 3 bit messages"):
  with members:
    - "minimizing message size in stochastic communication patterns fast self stabilizing protocols with 3 bits"
    - "noisy information spreading and plurality consensus"
    - "self stabilizing repeated balls into bins"
    - "towards optimal synchronous counting"
    - "stable leader election in population protocols requires linear time"
    - "stabilizing consensus with many opinions"
    - "plurality consensus in the gossip model"
    - "synchronous counting and computational algorithm design"

Connected through hyperedge HE-8 (induced by source paper "minimizing message size in stochastic communication patterns fast self stabilizing protocols with 3 bits"):
  with members:
    - "efficient k party voting with two choices"
    - "self stabilizing repeated balls into bins"
    - "efficient counting with optimal resilience"
    - "towards optimal synchronous counting"
    - "stable leader election in population protocols requires linear time"
    - "stabilizing consensus with many opinions"
    - "plurality consensus in the gossip model"
    - "synchronous counting and computational algorithm design"

Question: Please tell me which class the center node 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)