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: "optimal parameter choices through self adjustment applying the 1 5 th rule in discrete settings"
  Abstract: While evolutionary algorithms are known to be very successful for a broad range of applications, the algorithm designer is often left with many algorithmic choices, for example, the size of the population, the mutation rates, and the crossover rates of the algorithm. These parameters are known to have a crucial influence on the optimization time, and thus need to be chosen carefully, a task that often requires substantial efforts. Moreover, the optimal parameters can change during the optimization process. It is therefore of great interest to design mechanisms that dynamically choose best-possible parameters. An example for such an update mechanism is the one-fifth success rule for step-size adaption in evolutionary strategies. While in continuous domains this principle is well understood also from a mathematical point of view, no comparable theory is available for problems in discrete domains. #R##N#In this work we show that the one-fifth success rule can be effective also in discrete settings. We regard the $(1+(\lambda,\lambda))$~GA proposed in [Doerr/Doerr/Ebel: From black-box complexity to designing new genetic algorithms, TCS 2015]. We prove that if its population size is chosen according to the one-fifth success rule then the expected optimization time on \textsc{OneMax} is linear. This is better than what \emph{any} static population size $\lambda$ can achieve and is asymptotically optimal also among all adaptive parameter choices.

Connected through hyperedge HE-1 (induced by source paper "runtime performances of randomized search heuristics for the dynamic weighted vertex cover problem"):
  with members:
    - "parameterized analysis of multi objective evolutionary algorithms and the weighted vertex cover problem"
    - "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling"
    - "multiplicative drift analysis"
    - "chernoff hoeffding inequality and applications"

Connected through hyperedge HE-2 (induced by source paper "runtime analysis for self adaptive mutation rates"):
  with members:
    - "theory of parameter control for discrete black box optimization provable performance gains through dynamic parameter choices"
    - "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"
    - "level based analysis of genetic algorithms and other search processes"

Connected through hyperedge HE-3 (induced by source paper "complexity theory for discrete black box optimization heuristics"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "the 1 1 elitist black box complexity of leadingones"
    - "onemax in black box models with several restrictions"
    - "ranking based black box complexity"
    - "reducing the arity in unbiased black box complexity"
    - "a new method for lower bounds on the running time of evolutionary algorithms"
    - "playing mastermind with constant size memory"

Connected through hyperedge HE-4 (induced by source paper "a general dichotomy of evolutionary algorithms on monotone functions"):
  with members:
    - "fast genetic algorithms"
    - "optimal parameter settings for the 1 lambda lambda genetic algorithm"
    - "a tight runtime analysis of the 1 lambda lambda genetic algorithm on onemax"
    - "matchings with lower quotas algorithms and complexity"
    - "multiplicative drift analysis"
    - "optimizing monotone functions can be difficult"

Connected through hyperedge HE-5 (induced by source paper "better fixed arity unbiased black box algorithms"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "ranking based black box complexity"
    - "reducing the arity in unbiased black box complexity"
    - "playing mastermind with constant size memory"
    - "faster black box algorithms through higher arity operators"

Connected through hyperedge HE-6 (induced by source paper "discrepancy based evolutionary diversity optimization"):
  with members:
    - "evolutionary image composition using feature covariance matrices"
    - "feature based diversity optimization for problem instance classification"
    - "constructing low star discrepancy point sets with genetic algorithms"

Connected through hyperedge HE-7 (induced by source paper "the 1 λ evolutionary algorithm with self adjusting mutation rate"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "runtime analysis for self adaptive mutation rates"
    - "runtime analysis of the 1 λ λ genetic algorithm on random satisfiable 3 cnf formulas"
    - "the right mutation strength for multi valued decision variables"
    - "self adaptation of mutation rates in non elitist populations"
    - "multiplicative drift analysis"
    - "adaptive population models for offspring populations and parallel evolutionary algorithms"

Connected through hyperedge HE-8 (induced by source paper "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"):
  with members:
    - "fast genetic algorithms"
    - "escaping local optima using crossover with emergent or reinforced diversity"
    - "the right mutation strength for multi valued decision variables"
    - "playing mastermind with many colors"
    - "a new method for lower bounds on the running time of evolutionary algorithms"
    - "playing mastermind with constant size memory"

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)