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 "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.

Includes members:
  Member "plurality consensus in the gossip model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed voting in beep model")
  Member "on the influence of graph density on randomized gossiping":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mining frequent items in unstructured p2p networks")
  Member "discovery through gossip":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "strengthening gossip protocols using protocol dependent knowledge")
  Member "tight thresholds for cuckoo hashing via xorsat":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "peeling close to the orientability threshold spatial coupling in hashing based data structures")

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)