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: "on the effectiveness of simple success based parameter selection mechanisms for two classical discrete black box optimization benchmark problems"
  Abstract: Despite significant empirical and theoretically supported evidence that non-static parameter choices can be strongly beneficial in evolutionary computation, the question how to best adjust parameter values plays only a marginal role in contemporary research on discrete black-box optimization. This has led to the unsatisfactory situation in which feedback-free parameter selection rules such as the cooling schedule of Simulated Annealing are predominant in state-of-the-art heuristics, while, at the same time, we understand very well that such time-dependent selection rules can only perform worse than adjustment rules that do take into account the evolution of the optimization process. A number of adaptive and self-adaptive parameter control strategies have been proposed in the literature, but did not (yet) make their way to a broader public. A key obstacle seems to lie in their rather complex update rules. #R##N#The purpose of our work is to demonstrate that high-performing online parameter selection rules do not have to be very complicated. More precisely, we experiment with a multiplicative, comparison-based update rule to adjust the mutation probability of a (1+1)~Evolutionary Algorithm. We show that this simple self-adjusting rule outperforms the best static unary unbiased black-box algorithm on LeadingOnes, achieving an almost optimal speedup of about~$18\%$.

Connected through hyperedge HE-1 (induced by source paper "interpolating local and global search by controlling the variance of standard bit mutation"):
  with members:
    - "maximizing drift is not optimal for solving onemax"
    - "optimal parameter choices via precise black box analysis"
    - "fast artificial immune systems"
    - "better runtime guarantees via stochastic domination"
    - "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "iohprofiler a benchmarking and profiling tool for iterative optimization heuristics"
    - "fast genetic algorithms"

Connected through hyperedge HE-2 (induced by source paper "self adjusting mutation rates with provably optimal success rules"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "better runtime guarantees via stochastic domination"
    - "hyper heuristics can achieve optimal performance for pseudo boolean optimisation"
    - "runtime analysis for self adaptive mutation rates"
    - "adaptive drift analysis"
    - "multiplicative drift analysis"
    - "adaptive population models for offspring populations and parallel evolutionary algorithms"
    - "a new method for lower bounds on the running time of evolutionary algorithms"

Connected through hyperedge HE-3 (induced by source paper "offspring population size matters when comparing evolutionary algorithms with self adjusting mutation rates"):
  with members:
    - "self adjusting mutation rates with provably optimal success rules"
    - "optimal parameter choices via precise black box analysis"
    - "complexity theory for discrete black box optimization heuristics"
    - "towards a theory guided benchmarking suite for discrete black box optimization heuristics profiling 1 λ ea variants on onemax and leadingones"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "towards a more practice aware runtime analysis of evolutionary algorithms"
    - "adaptive population models for offspring populations and parallel evolutionary algorithms"

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)