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: "analysis of estimation of distribution algorithms and genetic algorithms on nk landscapes"
  Abstract: This study analyzes performance of several genetic and evolutionary algorithms on randomly generated NK fitness landscapes with various values of n and k. A large number of NK problem instances are first generated for each n and k, and the global optimum of each instance is obtained using the branch-and-bound algorithm. Next, the hierarchical Bayesian optimization algorithm (hBOA), the univariate marginal distribution algorithm (UMDA), and the simple genetic algorithm (GA) with uniform and two-point crossover operators are applied to all generated instances. Performance of all algorithms is then analyzed and compared, and the results are discussed.

Connected through hyperedge HE-1 (induced by source paper "on resampling vs adjusting probabilistic graphical models in estimation of distribution algorithms"):
  with members:
    - "on the performance of multi objective estimation of distribution algorithms for combinatorial problems"
    - "efficiently learning ising models on arbitrary graphs"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "information theoretic limits of selecting binary graphical models in high dimensions"

Connected through hyperedge HE-2 (induced by source paper "denoising autoencoders for fast combinatorial black box optimization"):
  with members:
    - "scalability of using restricted boltzmann machines for combinatorial optimization"
    - "sequence to sequence learning with neural networks"
    - "generalized denoising auto encoders as generative models"
    - "what regularized auto encoders learn from the data generating distribution"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-3 (induced by source paper "deep boltzmann machines in estimation of distribution algorithms for combinatorial optimization"):
  with members:
    - "denoising autoencoders for fast combinatorial black box optimization"
    - "sequence to sequence learning with neural networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-4 (induced by source paper "generative adversarial networks in estimation of distribution algorithms for combinatorial optimization"):
  with members:
    - "deep boltzmann machines in estimation of distribution algorithms for combinatorial optimization"
    - "denoising autoencoders for fast combinatorial black box optimization"
    - "generalized denoising auto encoders as generative models"

Connected through hyperedge HE-5 (induced by source paper "tunably rugged landscapes with known maximum and minimum"):
  with members:
    - "an analysis of phase transition in nk landscapes"

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)