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 "ergodic effects in token circulation"):
  Abstract: We consider a dynamical process in a network which distributes all tokens located at a node among its neighbors, in a round-robin manner. #R##N#We show that in the recurrent state of this dynamics, the number of particles located on a given edge, averaged over an interval of time, is tightly concentrated around the average particle density in the system. Formally, for a system of $k$ particles in a graph of $m$ edges, during any interval of length $T$, this time-averaged value is $k/m \pm \widetilde{O}(1/T)$, whenever $\gcd(m,k) = \widetilde{O}(1)$ (and so, e.g., whenever $m$ is a prime number). To achieve these bounds, we link the behavior of the studied dynamics to ergodic properties of traversals based on Eulerian circuits on a symmetric directed graph. These results are proved through sum set methods and are likely to be of independent interest. #R##N#As a corollary, we also obtain bounds on the \emph{idleness} of the studied dynamics, i.e., on the longest possible time between two consecutive appearances of a token on an edge, taken over all edges. Designing trajectories for $k$ tokens in a way which minimizes refresh time is fundamental to the study of patrolling in networks. Our results immediately imply a bound of $\widetilde{O}(m/k)$ on the idleness of the studied process, thus showing that it is a distributed $\widetilde{O}(1)$-competitive solution to the patrolling task, for all of the covered cases.

Includes members:
  Member "total variation discrepancy of deterministic random walks for ergodic markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the cover time of deterministic random walks for general transition probabilities")
  Member "improved analysis of deterministic load balancing schemes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrepancy analysis of a new randomized diffusion algorithm for weighted round matrices")
  Member "quasirandom load balancing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrepancy analysis of a new randomized diffusion algorithm for weighted round matrices")
  Member "tight bounds for randomized load balancing on arbitrary network topologies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel balanced allocations the heavily loaded case")

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)