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 "upper bounds on the running time of the univariate marginal distribution algorithm on onemax"):
  Abstract: The Univariate Marginal Distribution Algorithm (UMDA) is a randomized search heuristic that builds a stochastic model of the underlying optimization problem by repeatedly sampling #N#                  #N#                    #N#                  #N#                  $$\lambda $$#N#                  #N#                    #N#                  #N#                 solutions and adjusting the model according to the best #N#                  #N#                    #N#                  #N#                  $$\mu $$#N#                  #N#                    #N#                  #N#                 samples. We present a running time analysis of the UMDA on the classical OneMax benchmark function for wide ranges of the parameters #N#                  #N#                    #N#                  #N#                  $$\mu $$#N#                  #N#                    #N#                  #N#                 and #N#                  #N#                    #N#                  #N#                  $$\lambda $$#N#                  #N#                    #N#                  #N#                . If #N#                  #N#                    #N#                  #N#                  $$\mu \ge c\log n$$#N#                  #N#                    #N#                  #N#                 for some constant #N#                  #N#                    #N#                  #N#                  $$c>0$$#N#                  #N#                    #N#                  #N#                 and #N#                  #N#                    #N#                  #N#                  $$\lambda =(1+\varTheta (1))\mu $$#N#                  #N#                    #N#                  #N#                , we obtain a general bound #N#                  #N#                    #N#                  #N#                  $$O(\mu n)$$#N#                  #N#                    #N#                  #N#                 on the expected running time. This bound crucially assumes that all marginal probabilities of the algorithm are confined to the interval #N#                  #N#                    #N#                  #N#                  $$[1/n,1-1/n]$$#N#                  #N#                    #N#                  #N#                . If #N#                  #N#                    #N#                  #N#                  $$\mu \ge c' \sqrt{n}\log n$$#N#                  #N#                    #N#                  #N#                 for a constant #N#                  #N#                    #N#                  #N#                  $$c'>0$$#N#                  #N#                    #N#                  #N#                 and #N#                  #N#                    #N#                  #N#                  $$\lambda =(1+\varTheta (1))\mu $$#N#                  #N#                    #N#                  #N#                , the behavior of the algorithm changes and the bound on the expected running time becomes #N#                  #N#                    #N#                  #N#                  $$O(\mu \sqrt{n})$$#N#                  #N#                    #N#                  #N#                , which typically holds even if the borders on the marginal probabilities are omitted. The results supplement the recently derived lower bound #N#                  #N#                    #N#                  #N#                  $$\varOmega (\mu \sqrt{n}+n\log n)$$#N#                  #N#                    #N#                  #N#                 by Krejca and Witt (Proceedings of FOGA 2017, ACM Press, New York, pp 65–79, 2017) and turn out to be tight for the two very different choices #N#                  #N#                    #N#                  #N#                  $$\mu =c\log n$$#N#                  #N#                    #N#                  #N#                 and #N#                  #N#                    #N#                  #N#                  $$\mu =c'\sqrt{n}\log n$$#N#                  #N#                    #N#                  #N#                . They also improve the previously best known upper bound #N#                  #N#                    #N#                  #N#                  $$O(n\log n\log \log n)$$#N#                  #N#                    #N#                  #N#                 by Dang and Lehre (Proceedings of GECCO ’15, ACM Press, New York, pp 513–518, 2015) that was established for #N#                  #N#                    #N#                  #N#                  $$\mu =c\log n$$#N#                  #N#                    #N#                  #N#                 and #N#                  #N#                    #N#                  #N#                  $$\lambda =(1+\varTheta (1))\mu $$#N#                  #N#                    #N#                  #N#                .

Includes members:
  Member "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "update strength in edas and aco how to avoid genetic drift":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "a new method for lower bounds on the running time of evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")

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.NE(Neural and Evolutionary Computing)