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: "self stabilizing repeated balls into bins"
  Abstract: We study the following synchronous process that we call \emph {repeated balls-into-bins}. The process is started by assigning $n$ balls to $n$ bins in an arbitrary way. Then, in every subsequent round, one ball is chosen according to some fixed strategy (random, FIFO, etc) from each non-empty bin, and re-assigned to one of the $n$ bins uniformly at random. This process corresponds to a non-reversible Markov chain and our aim is to study its \emph {self-stabilization} properties with respect to the \emph{maximum (bin) load} and some related performance measures. #R##N#We define a configuration (i.e., a state) \emph{legitimate} if its maximum load is $O(\log n)$. We first prove that, starting from any legitimate configuration, the process will only take on legitimate configurations over a period of length bounded by \emph{any} polynomial in $n$, \emph{with high probability} (w.h.p.). Further we prove that, starting from \emph{any} configuration, the process converges to a legitimate configuration in linear time, w.h.p. This implies that the process is self-stabilizing w.h.p. and, moreover, that every ball traverses all bins in $O(n\log^2 n)$ rounds, w.h.p. The latter result can also be interpreted as an almost tight bound on the \emph{cover time} for the problem of \emph{parallel resource assignment} in the complete graph.

Connected through hyperedge HE-1 (induced by source paper "tight load balancing via randomized local search"):
  with members:
    - "threshold load balancing in networks"
    - "load balancing via random local search in closed and open systems"
    - "distributed selfish load balancing"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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"
    - "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"
    - "synchronous counting and computational algorithm design"

Connected through hyperedge HE-4 (induced by source paper "probabilistic self stabilization"):
  with members:
    - "plurality consensus in the gossip model"
    - "on the influence of graph density on randomized gossiping"
    - "simple dynamics for plurality consensus"

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)