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: "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"
  Abstract: Explaining to what extent the real power of genetic algorithms lies in the ability of crossover to recombine individuals into higher quality solutions is an important problem in evolutionary computation. In this paper we show how the interplay between mutation and crossover can make genetic algorithms hillclimb faster than their mutation-only counterparts. We devise a Markov Chain framework that allows to rigorously prove an upper bound on the runtime of standard steady state genetic algorithms to hillclimb the OneMax function. The bound establishes that the steady-state genetic algorithms are 25% faster than all standard bit mutation-only evolutionary algorithms with static mutation rate up to lower order terms for moderate population sizes. The analysis also suggests that larger populations may be faster than populations of size 2. We present a lower bound for a greedy (2+1) GA that matches the upper bound for populations larger than 2, rigorously proving that 2 individuals cannot outperform larger population sizes under greedy selection and greedy crossover up to lower order terms. In complementary experiments the best population size is greater than 2 and the greedy genetic algorithms are faster than standard ones, further suggesting that the derived lower bound also holds for the standard steady state (2+1) GA.

Connected through hyperedge HE-1 (induced by source paper "computational complexity analysis of genetic programming"):
  with members:
    - "evolving boolean functions with conjunctions and disjunctions via genetic programming"
    - "on inversely proportional hypermutations with mutation potential"
    - "where are we now a large benchmark study of recent symbolic regression methods"
    - "bounding bloat in genetic programming"
    - "destructiveness of lexicographic parsimony pressure and alleviation by a concatenation crossover in genetic programming"
    - "level based analysis of genetic algorithms and other search processes"
    - "computational complexity analysis of multi objective genetic programming"
    - "computational complexity analysis of simple genetic programming on two problems modeling isolated program semantics"

Connected through hyperedge HE-2 (induced by source paper "the benefits of population diversity in evolutionary algorithms a survey of rigorous runtime analyses"):
  with members:
    - "populations can be essential in tracking dynamic optima"
    - "fitness uniform optimization"

Connected through hyperedge HE-3 (induced by source paper "on the benefits of populations on the exploitation speed of standard steady state genetic algorithms"):
  with members:
    - "optimal parameter choices via precise black box analysis"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "level based analysis of genetic algorithms and other search processes"

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:
    - "theory of parameter control for discrete black box optimization provable performance gains through dynamic parameter choices"
    - "self adjusting mutation rates with provably optimal success rules"
    - "better runtime guarantees via stochastic domination"
    - "significance based estimation of distribution 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"

Connected through hyperedge HE-5 (induced by source paper "artificial immune systems can find arbitrarily good approximations for the np hard partition problem"):
  with members:
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "fast genetic algorithms"
    - "approximating optimization problems using eas on scale free networks"
    - "on the runtime of randomized local search and simple evolutionary algorithms for dynamic makespan scheduling"
    - "level based analysis of genetic algorithms and other search processes"

Connected through hyperedge HE-6 (induced by source paper "when hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms"):
  with members:
    - "fast artificial immune systems"
    - "artificial immune systems can find arbitrarily good approximations for the np hard partition problem"
    - "a general dichotomy of evolutionary algorithms on monotone functions"
    - "fast genetic algorithms"
    - "a new method for lower bounds on the running time of evolutionary algorithms"

Connected through hyperedge HE-7 (induced by source paper "fast artificial immune systems"):
  with members:
    - "when hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms"
    - "fast genetic algorithms"

Connected through hyperedge HE-8 (induced by source paper "level based analysis of the population based incremental learning algorithm"):
  with members:
    - "improved runtime bounds for the univariate marginal distribution algorithm via anti concentration"
    - "level based analysis of genetic algorithms and other search processes"

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)