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 "noisy rumor spreading and plurality consensus"):
  Abstract: Error-correcting codes are efficient methods for handling \emph{noisy} communication channels in the context of technological networks. However, such elaborate methods differ a lot from the unsophisticated way biological entities are supposed to communicate. Yet, it has been recently shown by Feinerman, Haeupler, and Korman {[}PODC 2014{]} that complex coordination tasks such as \emph{rumor spreading} and \emph{majority consensus} can plausibly be achieved in biological systems subject to noisy communication channels, where every message transferred through a channel remains intact with small probability $\frac{1}{2}+\epsilon$, without using coding techniques. This result is a considerable step towards a better understanding of the way biological entities may cooperate. It has been nevertheless be established only in the case of 2-valued \emph{opinions}: rumor spreading aims at broadcasting a single-bit opinion to all nodes, and majority consensus aims at leading all nodes to adopt the single-bit opinion that was initially present in the system with (relative) majority. In this paper, we extend this previous work to $k$-valued opinions, for any $k\geq2$. #R##N#Our extension requires to address a series of important issues, some conceptual, others technical. We had to entirely revisit the notion of noise, for handling channels carrying $k$-\emph{valued} messages. In fact, we precisely characterize the type of noise patterns for which plurality consensus is solvable. Also, a key result employed in the bivalued case by Feinerman et al. is an estimate of the probability of observing the most frequent opinion from observing the mode of a small sample. We generalize this result to the multivalued case by providing a new analytical proof for the bivalued case that is amenable to be extended, by induction, and that is of independent interest.

Includes members:
  Member "global majority consensus by local majority polling on graphs of a given degree sequence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication cost of consensus for nodes with limited memory")
  Member "limits for rumor spreading in stochastic populations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol")
  Member "space optimal majority in population protocols":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust comparison in population protocols")
  Member "minimizing message size in stochastic communication patterns fast self stabilizing protocols with 3 bits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "noisy information spreading and plurality consensus":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self stabilizing clock synchronization with 3 bit messages")
  Member "the power of two choices in distributed voting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed voting in beep model")
  Member "plurality consensus in the gossip model":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "distributed voting in beep model")
  Member "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol")

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)