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 "precise runtime analysis for plateaus"):
  Abstract: To gain a better theoretical understanding of how evolutionary algorithms cope with plateaus of constant fitness, we analyze how the $(1 + 1)$~EA optimizes the $n$-dimensional $Plateau_k$ function. This function has a plateau of second-best fitness in a radius of $k$ around the optimum. As optimization algorithm, we regard the $(1 + 1)$~EA using an arbitrary unbiased mutation operator. Denoting by $\alpha$ the random number of bits flipped in an application of this operator and assuming $\Pr[\alpha = 1] = \Omega(1)$, we show the surprising result that for $k \ge 2$ the expected optimization time of this algorithm is \[\frac{n^k}{k!\Pr[1 \le \alpha \le k]}(1 + o(1)),\] that is, the size of the plateau times the expected waiting time for an iteration flipping between $1$ and $k$ bits. Our result implies that the optimal mutation rate for this function is approximately~$k/en$. Our main analysis tool is a combined analysis of the Markov chains on the search point space and on the Hamming level space, an approach that promises to be useful also for other plateau problems.

Includes members:
  Member "theory of parameter control for discrete black box optimization provable performance gains through dynamic parameter choices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards white box benchmarks for algorithm control")
  Member "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help")
  Member "multiplicative up drift":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help")
  Member "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the limitations of the univariate marginal distribution algorithm to deception and where bivariate edas might help")
  Member "sharp bounds on the runtime of the 1 1 ea via drift analysis and analytic combinatorial tools":
    also connected through other hyperedges:
      (none)
  Member "optimal parameter choices via precise black box analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "fast genetic algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "level based analysis of genetic algorithms and other search processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)