human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "limited evaluation evolutionary optimization of large neural networks"):
  Abstract: Stochastic gradient descent is the most prevalent algorithm to train neural networks. However, other approaches such as evolutionary algorithms are also applicable to this task. Evolutionary algorithms bring unique trade-offs that are worth exploring, but computational demands have so far restricted exploration to small networks with few parameters. We implement an evolutionary algorithm that executes entirely on the GPU, which allows to efficiently batch-evaluate a whole population of networks. Within this framework, we explore the limited evaluation evolutionary algorithm for neural network training and find that its batch evaluation idea comes with a large accuracy trade-off. In further experiments, we explore crossover operators and find that unprincipled random uniform crossover performs extremely well. Finally, we train a network with 92k parameters on MNIST using an EA and achieve 97.6 % test accuracy compared to 98 % test accuracy on the same network trained with Adam. Code is available at this https URL.

Includes members:
  Member "limited evaluation cooperative co evolutionary differential evolution for large scale neuroevolution":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regularized evolutionary algorithm for dynamic neural topology search")
  Member "large scale evolution of image classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "hierarchical representations for efficient architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "large scale evolution of convolutional neural networks using volunteer computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "investigating recurrent neural network memory structures using neuro evolution")
  Member "on the relationship between the openai evolution strategy and stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions")
  Member "batch normalization accelerating deep network training by reducing internal covariate shift":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)