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: "self adaptive surrogate assisted covariance matrix adaptation evolution strategy"
  Abstract: This paper presents a novel mechanism to adapt surrogate-assisted population-based algorithms. This mechanism is applied to ACM-ES, a recently proposed surrogate-assisted variant of CMA-ES. The resulting algorithm, saACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters. Both heuristics significantly improve the quality of the surrogate model, yielding a significant speed-up of saACM-ES compared to the ACM-ES and CMA-ES baselines. The empirical validation of saACM-ES on the BBOB-2012 noiseless testbed demonstrates the efficiency and the scalability w.r.t the problem dimension and the population size of the proposed approach, that reaches new best results on some of the benchmark problems.

Connected through hyperedge HE-1 (induced by source paper "policy evaluation networks"):
  with members:
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "the value function polytope in reinforcement learning"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "value prediction network"
    - "a distributional perspective on reinforcement learning"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a new taxonomy of continuous global optimization algorithms"):
  with members:
    - "deep learning in neural networks"
    - "spot an r package for automatic and interactive tuning of optimization algorithms by sequential parameter optimization"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "cma es for hyperparameter optimization of deep neural networks"):
  with members:
    - "online batch selection for faster training of neural networks"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-4 (induced by source paper "lm cma an alternative to l bfgs for large scale black box optimization"):
  with members:
    - "maximum likelihood based online adaptation of hyper parameters in cma es"
    - "a computationally efficient limited memory cma es for large scale optimization"
    - "linear convergence of comparison based step size adaptive randomized search via stability of markov chains"
    - "objective improvement in information geometric optimization"
    - "a linear time natural evolution strategy for non separable functions"

Connected through hyperedge HE-5 (induced by source paper "maximum likelihood based online adaptation of hyper parameters in cma es"):
  with members:
    - "alternative restart strategies for cma es"

Connected through hyperedge HE-6 (induced by source paper "a computationally efficient limited memory cma es for large scale optimization"):
  with members:
    - "a linear time natural evolution strategy for non separable functions"

Connected through hyperedge HE-7 (induced by source paper "black box optimization benchmarking of ipop saacm es and bipop saacm es on the bbob 2012 noiseless testbed"):
  with members:
    - "alternative restart strategies for cma es"

Connected through hyperedge HE-8 (induced by source paper "alternative restart strategies for cma es"):
  with members:
    - "black box optimization benchmarking of ipop saacm es and bipop saacm es on the bbob 2012 noiseless testbed"

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)