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: "fitness uniform optimization"
  Abstract: In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of fitter individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be able to escape from local optima on the other hand. Motivated by a universal similarity relation on the individuals, we propose a new selection scheme, which is uniform in the fitness values. It generates selection pressure toward sparsely populated fitness regions, not necessarily toward higher fitness, as is the case for all other selection schemes. We show analytically on a simple example that the new selection scheme can be much more effective than standard selection schemes. We also propose a new deletion scheme which achieves a similar result via deletion and show how such a scheme preserves genetic diversity more effectively than standard approaches. We compare the performance of the new schemes to tournament selection and random deletion on an artificial deceptive problem and a range of NP hard problems: traveling salesman, set covering, and satisfiability

Connected through hyperedge HE-1 (induced by source paper "frequency fitness assignment making optimization algorithms invariant under bijective transformations of the objective function"):
  with members:
    - "quality diversity through surprise"
    - "evaluating map elites on constrained optimization problems"
    - "precise runtime analysis for plateaus"
    - "towards a more practice aware runtime analysis of evolutionary algorithms"
    - "quality and diversity optimization a unifying modular framework"
    - "illuminating search spaces by mapping elites"
    - "a tabu search path relinking algorithm to solve the job shop scheduling problem"
    - "novelty search in competitive coevolution"

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:
    - "standard steady state genetic algorithms can hillclimb faster than mutation only evolutionary algorithms"
    - "populations can be essential in tracking dynamic optima"

Connected through hyperedge HE-3 (induced by source paper "on the use of diversity mechanisms in dynamic constrained continuous optimization"):
  with members:
    - "the benefits of population diversity in evolutionary algorithms a survey of rigorous runtime analyses"
    - "a comparison of constraint handling techniques for dynamic constrained optimization problems"

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)