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: "critical control of a genetic algorithm"
  Abstract: Based on speculations coming from statistical mechanics and the conjectured existence of critical states, I propose a simple heuristic in order to control the mutation probability and the population size of a genetic algorithm. Genetic algorithms are widely used nowadays, as well as their cousins evolutionary algorithms. The most cited initial references on genetic algorithms are the beautiful books of Holland [10], who tried to initiate a theoretical analysis of these processes, and of Goldberg [9], who made a very attractive exposition of these algorithms. The literature on genetic algorithms is now so huge that it is beyond my ability to compile a decent reasonable review. For years, there has been an urgent and growing demand for guidelines to operate a genetic algorithm on a practical problem. On the theoretical side, progress is quite slow and somehow disappointing for practitioners. The theoretical works often deal with a simple toy problem, otherwise the behavior of the genetic algorithm is too complex to be amenable to rigorous mathematical analysis. Here I propose a simple heuristic in order to control efficiently the genetic algorithm, based on speculations coming from statistical mechanics and the conjectured existence of critical states. Although it is quite simple, I have not been able to locate this heuristic in the literature, and I hope it will be useful. Apart from my own belief, it is supported by several empirical studies, the most notable one being the work of Ochoa [13], and it is in accordance with several conclusions and ideas appearing in the work of van Nimwegen and Crutchfield [17]. Error threshold. The fundamental notion on which the heuristic is based is the notion of error threshold, introduced by Manfred Eigen in 1971 [6]. Eigen analyzed a simple system of replicating molecules and demonstrated

Connected through hyperedge HE-1 (induced by source paper "the quasispecies regime for the simple genetic algorithm with roulette wheel selection"):
  with members:
    - "level based analysis of genetic algorithms and other search processes"
    - "on the impact of mutation selection balance on the runtime of 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)