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: "a discipline of evolutionary programming"
  Abstract: Genetic fitness optimization using small populations or small population updates across generations generally suffers from randomly diverging evolutions. We propose a notion of highly probable fitness optimization through feasible evolutionary computing runs on small size populations. Based on rapidly mixing Markov chains, the approach pertains to most types of evolutionary genetic algorithms, genetic programming and the like. We establish that for systems having associated rapidly mixing Markov chains and appropriate stationary distributions the new method finds optimal programs (individuals) with probability almost 1. To make the method useful would require a structured design methodology where the development of the program and the guarantee of the rapidly mixing property go hand in hand. We analyze a simple example to show that the method is implementable. More significant examples require theoretical advances, for example with respect to the Metropolis filter.

Connected through hyperedge HE-1 (induced by source paper "hitting times of local and global optima in genetic algorithms with very high selection pressure"):
  with members:
    - "level based analysis of genetic algorithms and other search processes"
    - "non elitist genetic algorithm as a local search method"
    - "a new method for lower bounds on the running time of evolutionary algorithms"

Connected through hyperedge HE-2 (induced by source paper "on proportions of fit individuals in population of evolutionary algorithm with tournament selection"):
  with members:
    - "level based analysis of genetic algorithms and other search processes"
    - "non elitist genetic algorithm as a local search method"
    - "a new method for lower bounds on the running time of evolutionary algorithms"

Connected through hyperedge HE-3 (induced by source paper "level based analysis of genetic algorithms for combinatorial optimization"):
  with members:
    - "level based analysis of genetic algorithms and other search processes"
    - "non elitist genetic algorithm as a local search method"

Connected through hyperedge HE-4 (induced by source paper "a fast quartet tree heuristic for hierarchical clustering"):
  with members:
    - "analyzing worms and network traffic using compression"
    - "the google similarity distance"
    - "the similarity metric"

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)