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: "the benefits of population diversity in evolutionary algorithms a survey of rigorous runtime analyses"
  Abstract: Population diversity is crucial in evolutionary algorithms to enable global exploration and to avoid poor performance due to premature convergence. This chapter reviews runtime analyses that have shown benefits of population diversity, either through explicit diversity mechanisms or through naturally emerging diversity. These analyses show that the benefits of diversity are manifold: diversity is important for global exploration and the ability to find several global optima. Diversity enhances crossover and enables crossover to be more effective than mutation. Diversity can be crucial in dynamic optimization, when the problem landscape changes over time. And, finally, it facilitates the search for the whole Pareto front in evolutionary multiobjective optimization.

Connected through hyperedge HE-1 (induced by source paper "on the use of diversity mechanisms in dynamic constrained continuous optimization"):
  with members:
    - "a comparison of constraint handling techniques for dynamic constrained optimization problems"
    - "fitness uniform optimization"

Connected through hyperedge HE-2 (induced by source paper "on inversely proportional hypermutations with mutation potential"):
  with members:
    - "artificial immune systems can find arbitrarily good approximations for the np hard partition problem"
    - "when hypermutations and ageing enable artificial immune systems to outperform evolutionary algorithms"

Connected through hyperedge HE-3 (induced by source paper "runtime analysis of probabilistic crowding and restricted tournament selection for bimodal optimisation"):
  with members:
    - "probabilistic tools for the analysis of randomized optimization heuristics"
    - "erratum simplified drift analysis for proving lower bounds in evolutionary computation"

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)