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 "on the parallel undecided state dynamics with two colors"):
  Abstract: The \emph{Undecided-State Dynamics} is a well-known protocol that achieves \emph{Consensus} in distributed systems formed by a set of $n$ anonymous nodes interacting via a communication network. We consider this dynamics in the parallel \pull\ communication model on the complete graph for the binary case, i.e., when every node can either support one of two possible colors (say, \colA\ and \colB) or stay in the \emph{undecided} state. Previous work in this setting only considers initial color configurations with no undecided nodes and a large \emph{bias} (i.e., $\Theta(n)$) towards the majority color. A major open question here is whether this dynamics reaches consensus \emph{quickly}, i.e. within a polylogarithmic number of rounds. #R##N#In this paper we present an \textit{unconditional} analysis of the Undecided-State Dynamics which answers to the above question in the affermative. Our analysis shows that, starting from \textit{any} initial configuration, the Undecided-State Dynamics reaches a monochromatic configuration within $O(\log^2 n)$ rounds, with high probability (w.h.p.). %This bound is almost tight since a lower bound $\Omega(\log n)$ is known. Moreover, we prove that if the initial configuration has bias $\Omega(\sqrt{n\log n})$, then the dynamics converges toward the initial majority color within $O(\log n)$ round, w.h.p. #R##N#At the heart of our approach there is a new analysis of the \textit{symmetry-breaking} phase that the process must perform in order to escape from (almost-)unbiased configurations. Previous symmetry-breaking analysis of consensus dynamics essentially concern sequential communication models (such as \textit{Population Protocols}) and/or symmetric updated rules (such as \textit{majority} rules).

Includes members:
  Member "ignore or comply on breaking symmetry in consensus":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "noidy conmunixatipn on the convergence of the averaging population protocol")
  Member "tight analysis for the 3 majority consensus dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems")
  Member "bounds on the voter model in dynamic networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distributed community detection via metastability of the 2 choices dynamics")
  Member "stabilizing consensus with many opinions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the necessary memory to compute the plurality in multi agent systems")
  Member "the power of two choices in distributed voting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed voting in beep model")
  Member "plurality consensus in the gossip model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "distributed voting in beep model")
  Member "determining majority in networks with local interactions and very small local memory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust comparison in population protocols")
  Member "simple dynamics for plurality consensus":
    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.DS(Data Structures and Algorithms)