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 "unbiased black box complexities of jump functions"):
  Abstract: We analyze the unbiased black-box complexity of jump functions with small, medium, and large sizes of the fitness plateau surrounding the optimal solution. #R##N#Among other results, we show that when the jump size is $(1/2 - \varepsilon)n$, that is, only a small constant fraction of the fitness values is visible, then the unbiased black-box complexities for arities $3$ and higher are of the same order as those for the simple \textsc{OneMax} function. Even for the extreme jump function, in which all but the two fitness values $n/2$ and $n$ are blanked out, polynomial-time mutation-based (i.e., unary unbiased) black-box optimization algorithms exist. This is quite surprising given that for the extreme jump function almost the whole search space (all but a $\Theta(n^{-1/2})$ fraction) is a plateau of constant fitness. #R##N#To prove these results, we introduce new tools for the analysis of unbiased black-box complexities, for example, selecting the new parent individual not by comparing the fitnesses of the competing search points, but also by taking into account the (empirical) expected fitnesses of their offspring.

Includes members:
  Member "ranking based black box complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic tools for the analysis of randomized optimization heuristics")
  Member "reducing the arity in unbiased black box complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "black box complexity of the binary value function")
  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")
  Member "black box complexities of combinatorial problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal parameter choices via precise black box analysis")
  Member "playing mastermind with constant size memory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "black box complexity of the binary value function")
  Member "faster black box algorithms through higher arity operators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "benchmarking discrete optimization heuristics with iohprofiler")

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)