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: "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).

Connected through hyperedge HE-1 (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"

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.DS(Data Structures and Algorithms)