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: "benchmarking evolutionary algorithms for single objective real valued constrained optimization a critical review"
  Abstract: Abstract   Benchmarking plays an important role in the development of novel search algorithms as well as for the assessment and comparison of contemporary algorithmic ideas. This paper presents common principles that need to be taken into account when considering benchmarking problems for constrained optimization. Current benchmark environments for testing Evolutionary Algorithms are reviewed in the light of these principles. Along with this line, the reader is provided with an overview of the available problem domains in the field of constrained benchmarking. Hence, the review supports algorithms developers with information about the merits and demerits of the available frameworks.

Connected through hyperedge HE-1 (induced by source paper "recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms practical guidelines and a critical review"):
  with members:
    - "sample size estimation for power and accuracy in the experimental comparison of algorithms"
    - "classifier comparison using precision"
    - "statistical comparison of classifiers through bayesian hierarchical modelling"

Connected through hyperedge HE-2 (induced by source paper "evaluating map elites on constrained optimization problems"):
  with members:
    - "optimisation and illumination of a real world workforce scheduling and routing application via map elites"
    - "discovering the elite hypervolume by leveraging interspecies correlation"
    - "enhanced optimization with composite objectives and novelty selection"
    - "multi objective analysis of map elites performance"
    - "quality and diversity optimization a unifying modular framework"
    - "memetic viability evolution for constrained optimization"
    - "robots that can adapt like animals"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-3 (induced by source paper "a linear constrained optimization benchmark for probabilistic search algorithms the rotated klee minty problem"):
  with members:
    - "a covariance matrix self adaptation evolution strategy for optimization under linear constraints"

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)