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 "maximizing drift is not optimal for solving onemax"):
  Abstract: It seems very intuitive that for the maximization of the OneMax problem $f(x):=\sum_{i=1}^n{x_i}$ the best that an elitist unary unbiased search algorithm can do is to store a best so far solution, and to modify it with the operator that yields the best possible expected progress in function value. This assumption has been implicitly used in several empirical works. In [Doerr, Doerr, Yang: GECCO 2016] it was formally proven that this approach is indeed almost optimal. #R##N#In this work we prove that drift maximization is \emph{not} optimal. More precisely, we show that for most fitness levels $n/2 0}$. #R##N#As a result of independent interest we show that the optimal mutation strengths, unlike the drift-maximizing ones, can be even.

Includes members:
  Member "optimal parameter choices via precise black box analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "runtime analysis for self adaptive mutation rates":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "towards a more practice aware runtime analysis of evolutionary algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function")
  Member "multiplicative drift analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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)