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: "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#                .

Connected through hyperedge HE-1 (induced by source paper "working principles of binary differential evolution"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "fast re optimization via structural diversity"
    - "better runtime guarantees via stochastic domination"
    - "significance based estimation of distribution algorithms"
    - "level based analysis of the population based incremental learning algorithm"
    - "the query complexity of a permutation based variant of mastermind"
    - "update strength in edas and aco how to avoid genetic drift"
    - "ranking based black box complexity"

Connected through hyperedge HE-2 (induced by source paper "sharp bounds for genetic drift in edas"):
  with members:
    - "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help"
    - "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"
    - "update strength in edas and aco how to avoid genetic drift"

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.NE(Neural and Evolutionary Computing)