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: "a computationally efficient limited memory cma es for large scale optimization"
  Abstract: We propose a computationally efficient limited memory Covariance Matrix Adaptation Evolution Strategy for large scale optimization, which we call the LM-CMA-ES. The LM-CMA-ES is a stochastic, derivative-free algorithm for numerical optimization of non-linear, non-convex optimization problems in continuous domain. Inspired by the limited memory BFGS method of Liu and Nocedal (1989), the LM-CMA-ES samples candidate solutions according to a covariance matrix reproduced from $m$ direction vectors selected during the optimization process. The decomposition of the covariance matrix into Cholesky factors allows to reduce the time and memory complexity of the sampling to $O(mn)$, where $n$ is the number of decision variables. When $n$ is large (e.g., $n$ > 1000), even relatively small values of $m$ (e.g., $m=20,30$) are sufficient to efficiently solve fully non-separable problems and to reduce the overall run-time.

Connected through hyperedge HE-1 (induced by source paper "evolution attack on neural networks"):
  with members:
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "adversarial transformation networks learning to generate adversarial examples"
    - "adversarial examples for semantic segmentation and object detection"
    - "on the relationship between the openai evolution strategy and stochastic gradient descent"
    - "mask r cnn"
    - "practical black box attacks against machine learning"
    - "adversarial diversity and hard positive generation"
    - "universal adversarial perturbations"

Connected through hyperedge HE-2 (induced by source paper "an evolutionary algorithm of linear complexity application to training of deep neural networks"):
  with members:
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "deep learning in neural networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "challenges in high dimensional reinforcement learning with evolution strategies"):
  with members:
    - "parameter space noise for exploration"
    - "limited memory matrix adaptation for large scale black box optimization"
    - "es is more than just a traditional finite difference approximator"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "coco a platform for comparing continuous optimizers in a black box setting"
    - "a linear time natural evolution strategy for non separable functions"
    - "evolutionary algorithms for reinforcement learning"

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"
    - "linear convergence of comparison based step size adaptive randomized search via stability of markov chains"
    - "objective improvement in information geometric optimization"
    - "self adaptive surrogate assisted covariance matrix adaptation evolution strategy"
    - "a linear time natural evolution strategy for non separable functions"

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)