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: "large scale evolution of convolutional neural networks using volunteer computing"
  Abstract: This work presents a new algorithm called evolutionary exploration of augmenting convolutional topologies (EXACT), which is capable of evolving the structure of convolutional neural networks (CNNs). EXACT is in part modeled after the neuroevolution of augmenting topologies (NEAT) algorithm, with notable exceptions to allow it to scale to large scale distributed computing environments and evolve networks with convolutional filters. In addition to multithreaded and MPI versions, EXACT has been implemented as part of a BOINC volunteer computing project, allowing large scale evolution. During a period of two months, over 4,500 volunteered computers on the Citizen Science Grid trained over 120,000 CNNs and evolved networks reaching 98.32% test data accuracy on the MNIST handwritten digits dataset. These results are even stronger as the backpropagation strategy used to train the CNNs was fairly rudimentary (ReLU units, L2 regularization and Nesterov momentum) and these were initial test runs done without refinement of the backpropagation hyperparameters. Further, the EXACT evolutionary strategy is independent of the method used to train the CNNs, so they could be further improved by advanced techniques like elastic distortions, pretraining and dropout. The evolved networks are also quite interesting, showing "organic" structures and significant differences from standard human designed architectures.

Connected through hyperedge HE-1 (induced by source paper "investigating recurrent neural network memory structures using neuro evolution"):
  with members:
    - "accelerating the evolution of convolutional neural networks with node level mutations and epigenetic weight initialization"
    - "from nodes to networks evolving recurrent neural networks"
    - "perturbative neural networks"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "learning simpler language models with the differential state framework"
    - "evolving deep convolutional neural networks for image classification"
    - "a genetic programming approach to designing convolutional neural network architectures"

Connected through hyperedge HE-2 (induced by source paper "the ant swarm neuro evolution procedure for optimizing recurrent networks"):
  with members:
    - "a specialized evolutionary strategy using mean absolute error random sampling to design recurrent neural networks"
    - "investigating recurrent neural network memory structures using neuro evolution"
    - "low cost recurrent neural network expected performance evaluation"
    - "accelerating the evolution of convolutional neural networks with node level mutations and epigenetic weight initialization"
    - "evolving deep neural networks"
    - "learning simpler language models with the differential state framework"
    - "minimal gated unit for recurrent neural networks"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-3 (induced by source paper "evolutionary construction of convolutional neural networks"):
  with members:
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "evolving parsimonious networks by mixing activation functions"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "metaheuristic algorithms for convolution neural network"

Connected through hyperedge HE-4 (induced by source paper "genetic programming and gradient descent a memetic approach to binary image classification"):
  with members:
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional networks"
    - "deep inside convolutional networks visualising image classification models and saliency maps"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "boinc a platform for volunteer computing"):
  with members:
    - "comparative benchmarking of cloud computing vendors with high performance linpack"

Connected through hyperedge HE-6 (induced by source paper "auto keras an efficient neural architecture search system"):
  with members:
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "nas fpn learning scalable feature pyramid architecture for object detection"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"

Connected through hyperedge HE-7 (induced by source paper "born to learn the inspiration progress and future of evolved plastic artificial neural networks"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "evolving deep neural networks"
    - "lstm a search space odyssey"
    - "progressive neural networks"
    - "hierarchical representations for efficient architecture search"
    - "diffusion based neuromodulation can eliminate catastrophic forgetting in simple neural networks"
    - "pathnet evolution channels gradient descent in super neural networks"

Connected through hyperedge HE-8 (induced by source paper "autonomous deep learning a genetic dcnn designer for image classification"):
  with members:
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "dynamic routing between capsules"
    - "emnist an extension of mnist to handwritten letters"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "densely connected convolutional networks"

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)