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: "limited evaluation cooperative co evolutionary differential evolution for large scale neuroevolution"
  Abstract: Many real-world control and classification tasks involve a large number of features. When artificial neural networks (ANNs) are used for modeling these tasks, the network architectures tend to be large. Neuroevolution is an effective approach for optimizing ANNs; however, there are two bottlenecks that make their application challenging in case of high-dimensional networks using direct encoding. First, classic evolutionary algorithms tend not to scale well for searching large parameter spaces; second, the network evaluation over a large number of training instances is in general time-consuming. In this work, we propose an approach called the Limited Evaluation Cooperative Co-evolutionary Differential Evolution algorithm (LECCDE) to optimize high-dimensional ANNs.   The proposed method aims to optimize the pre-synaptic weights of each post-synaptic neuron in different subpopulations using a Cooperative Co-evolutionary Differential Evolution algorithm, and employs a limited evaluation scheme where fitness evaluation is performed on a relatively small number of training instances based on fitness inheritance. We test LECCDE on three datasets with various sizes, and our results show that cooperative co-evolution significantly improves the test error comparing to standard Differential Evolution, while the limited evaluation scheme facilitates a significant reduction in computing time.

Connected through hyperedge HE-1 (induced by source paper "regularized evolutionary algorithm for dynamic neural topology search"):
  with members:
    - "unsupervised domain adaptation using feature whitening and consensus loss"
    - "regularized evolution for image classifier architecture search"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "practical block wise neural network architecture generation"
    - "mask r cnn"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "evolving plasticity for autonomous learning under changing environmental conditions"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "learning with delayed synaptic plasticity"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"

Connected through hyperedge HE-3 (induced by source paper "learning with delayed synaptic plasticity"):
  with members:
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"

Connected through hyperedge HE-4 (induced by source paper "limited evaluation evolutionary optimization of large neural networks"):
  with members:
    - "large scale evolution of image classifiers"
    - "hierarchical representations for efficient architecture search"
    - "large scale evolution of convolutional neural networks using volunteer computing"
    - "on the relationship between the openai evolution strategy and stochastic gradient descent"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"

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)