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 selection to preserve genetic diversity"
  Abstract: In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of more fit 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. We propose a new selection scheme, which is uniform in the fitness values. It generates selection pressure towards sparsely populated fitness regions, not necessarily towards higher fitness, as is the case for all other selection schemes. We show that the new selection scheme can be much more effective than standard selection schemes.

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 "fitness uniform optimization"):
  with members:
    - "fitness uniform deletion a simple way to preserve diversity"
    - "tournament versus fitness uniform selection"

Connected through hyperedge HE-3 (induced by source paper "fitness uniform deletion a simple way to preserve diversity"):
  with members:
    - "tournament versus fitness uniform selection"

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.AI(Artificial Intelligence)