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: "minimizing message size in stochastic communication patterns fast self stabilizing protocols with 3 bits"
  Abstract: This paper considers the basic $\mathcal{PULL}$ model of communication, in which in each round, each agent extracts information from few randomly chosen agents. We seek to identify the smallest amount of information revealed in each interaction (message size) that nevertheless allows for efficient and robust computations of fundamental information dissemination tasks. We focus on the Majority Bit Dissemination problem that considers a population of $n$ agents, with a designated subset of source agents. Each source agent holds an input bit and each agent holds an output bit. The goal is to let all agents converge their output bits on the most frequent input bit of the sources (the majority bit). Note that the particular case of a single source agent corresponds to the classical problem of Broadcast. We concentrate on the severe fault-tolerant context of self-stabilization, in which a correct configuration must be reached eventually, despite all agents starting the execution with arbitrary initial states. #R##N#We first design a general compiler which can essentially transform any self-stabilizing algorithm with a certain property that uses $\ell$-bits messages to one that uses only $\log \ell$-bits messages, while paying only a small penalty in the running time. By applying this compiler recursively we then obtain a self-stabilizing Clock Synchronization protocol, in which agents synchronize their clocks modulo some given integer $T$, within $\tilde O(\log n\log T)$ rounds w.h.p., and using messages that contain $3$ bits only. #R##N#We then employ the new Clock Synchronization tool to obtain a self-stabilizing Majority Bit Dissemination protocol which converges in $\tilde O(\log n)$ time, w.h.p., on every initial configuration, provided that the ratio of sources supporting the minority opinion is bounded away from half. Moreover, this protocol also uses only 3 bits per interaction.

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"

Connected through hyperedge HE-2 (induced by source paper "limits for rumor spreading in stochastic populations"):
  with members:
    - "information theoretic lower bounds for distributed function computation"
    - "noisy rumor spreading and plurality consensus"
    - "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"
    - "global computation in a poorly connected world fast rumor spreading with no dependence on conductance"

Connected through hyperedge HE-3 (induced by source paper "robust detection in leak prone population protocols"):
  with members:
    - "spreading a confirmed rumor a case for oscillatory dynamics"
    - "simple and optimal randomized fault tolerant rumor spreading"
    - "two domain dna strand displacement"
    - "robust stochastic chemical reaction networks and bounded tau leaping"

Connected through hyperedge HE-4 (induced by source paper "self stabilizing clock synchronization with 3 bit messages"):
  with members:
    - "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"
    - "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"
    - "synchronous counting and computational algorithm design"

Connected through hyperedge HE-5 (induced by source paper "noisy rumor spreading and plurality consensus"):
  with members:
    - "global majority consensus by local majority polling on graphs of a given degree sequence"
    - "limits for rumor spreading in stochastic populations"
    - "space optimal majority in population protocols"
    - "noisy information spreading and plurality consensus"
    - "the power of two choices in distributed voting"
    - "plurality consensus in the gossip model"
    - "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"
    - "determining majority in networks with local interactions and very small local memory"

Connected through hyperedge HE-6 (induced by source paper "efficient counting with optimal resilience"):
  with members:
    - "towards optimal synchronous counting"
    - "synchronous counting and computational algorithm design"

Connected through hyperedge HE-7 (induced by source paper "towards optimal synchronous counting"):
  with members:
    - "near optimal self stabilising counting and firing squads"
    - "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)