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: "theory of parameter control for discrete black box optimization provable performance gains through dynamic parameter choices"
  Abstract: Parameter control is aimed at realizing performance gains through a dynamic choice of the parameters which determine the behavior of the underlying optimization algorithm. In the context of evolutionary algorithms, this research line has for a long time been dominated by empirical approaches. With the significant advances in running-time analysis achieved in the last ten years, the parameter control question has become accessible to theoretical investigations. A number of running-time results for a broad range of different parameter control mechanisms have been obtained in recent years. This chapter surveys these results, and puts them into context by proposing an updated classification scheme for parameter control.

Connected through hyperedge HE-1 (induced by source paper "towards white box benchmarks for algorithm control"):
  with members:
    - "rllib abstractions for distributed reinforcement learning"
    - "learning to learn by gradient descent by gradient descent"
    - "sgdr stochastic gradient descent with restarts"
    - "learning to optimize"
    - "asynchronous methods for deep reinforcement learning"
    - "layer specific adaptive learning rates for deep networks"
    - "the configurable sat solver challenge cssc"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "runtime analysis for self adaptive mutation rates"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "drift theory in continuous search spaces expected hitting time of the 1 1 es with 1 5 success rule"
    - "optimal parameter choices via precise black box analysis"
    - "better runtime guarantees via stochastic domination"
    - "the 1 λ evolutionary algorithm with self adjusting mutation rate"
    - "self adaptation of mutation rates in non elitist populations"
    - "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
    - "level based analysis of genetic algorithms and other search processes"

Connected through hyperedge HE-3 (induced by source paper "precise runtime analysis for plateaus"):
  with members:
    - "a tight runtime analysis for the cga on jump functions edas can cross fitness valleys at no extra cost"
    - "multiplicative up drift"
    - "an exponential lower bound for the runtime of the compact genetic algorithm on jump functions"
    - "sharp bounds on the runtime of the 1 1 ea via drift analysis and analytic combinatorial tools"
    - "optimal parameter choices via precise black box analysis"
    - "fast genetic algorithms"
    - "level based analysis of genetic algorithms and other search processes"
    - "unbiased black box complexities of jump functions"

Connected through hyperedge HE-4 (induced by source paper "simple hyper heuristics control the neighbourhood size of randomised local search optimally for leadingones"):
  with members:
    - "self adjusting mutation rates with provably optimal success rules"
    - "better runtime guarantees via stochastic domination"
    - "significance based estimation of distribution algorithms"
    - "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"
    - "the 1 1 elitist black box complexity of leadingones"
    - "level based analysis of genetic algorithms and other search processes"
    - "algorithm selection for combinatorial search problems a survey"
    - "pure strategy or mixed strategy"

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)