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: "denoising autoencoders for fast combinatorial black box optimization"
  Abstract: Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Autoencoders (AE) are generative stochastic networks with these desired properties. We integrate a special type of AE, the Denoising Autoencoder (DAE), into an EDA and evaluate the performance of DAE-EDA on several combinatorial optimization problems with a single objective. We asses the number of fitness evaluations as well as the required CPU times. We compare the results to the performance to the Bayesian Optimization Algorithm (BOA) and RBM-EDA, another EDA which is based on a generative neural network which has proven competitive with BOA. For the considered problem instances, DAE-EDA is considerably faster than BOA and RBM-EDA, sometimes by orders of magnitude. The number of fitness evaluations is higher than for BOA, but competitive with RBM-EDA. These results show that DAEs can be useful tools for problems with low but non-negligible fitness evaluation costs.

Connected through hyperedge HE-1 (induced by source paper "deep optimisation solving combinatorial optimisation problems using deep neural networks"):
  with members:
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "learning combinatorial optimization algorithms over graphs"
    - "neural combinatorial optimization with reinforcement learning"
    - "optimization by pairwise linkage detection incremental linkage set and restricted back mixing dsmga ii"
    - "a denoising autoencoder that guides stochastic search"
    - "an estimation of distribution algorithm with intelligent local search for rule based nurse rostering"

Connected through hyperedge HE-2 (induced by source paper "deep boltzmann machines in estimation of distribution algorithms for combinatorial optimization"):
  with members:
    - "sequence to sequence learning with neural networks"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "analysis of estimation of distribution algorithms and genetic algorithms on nk landscapes"

Connected through hyperedge HE-3 (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"
    - "generalized denoising auto encoders as generative models"
    - "analysis of estimation of distribution algorithms and genetic algorithms on 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)