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 "population protocols for leader election and exact majority with o log 2 n states and o log 2 n convergence time"):
  Abstract: We consider the model of population protocols, which can be viewed as a sequence of random pairwise interactions of $n$ agents (nodes). We show population protocols for two problems: the leader election and the exact majority voting. The leader election starts with all agents in the same initial state and the goal is to converge to the (global) state when exactly one agent is in a distinct state $L$. The exact majority voting starts with each agent in one of the two distinct states $A$ or $B$ and the goal is to make all nodes know which of these two states was the initial majority state, even if that majority was just by a single vote. #R##N#Alistarh and Gelashvili [ICALP 2015] showed a leader-election protocol which converges in $O(\log^3 n)$ time w.h.p. and in expectation and needs $\Theta(\log^3 n)$ states per agent. We present a protocol which elects the leader in $O(\log^2 n)$ time w.h.p. and in expectation and uses $\Theta(\log^2 n)$ states per agent. For the exact majority voting, we show a population protocol with the same asymptotic performance: $O(\log^2 n)$ time and $\Theta(\log^2 n)$ states per agent. The exact-majority protocol proposed by Alistarh et al. [PODC 2015] achieves expected $O(\log^2 n)$ time, but requires a relatively high initial imbalance between $A$'s and $B$'s or a large number of states per agent. More recently, Alistarh et al. [SODA 2017] showed $O(\log^2 n)$-state protocols for both problems, with the exact majority protocol converging in time $O(\log^3 n)$, and the leader election protocol converging in time $O(\log^{6.3} n)$ w.h.p. and $O(\log^{5.3} n)$ in expectation. #R##N#Our leader election and exact majority protocols are based on the idea of agents counting their local interactions and rely on the probabilistic fact that the uniform random selection would limit the divergence of the individual counts.

Includes members:
  Member "determining majority in networks with local interactions and very small local memory":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust comparison in population protocols")
  Member "coalescing random walks and voting on connected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interactive particle systems on hypergraphs drift analysis and the walksat algorithm")
  Member "tight bounds for randomized load balancing on arbitrary network topologies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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.DC(Distributed, Parallel, and Cluster Computing)