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: "generative neuroevolution for deep learning"
  Abstract: An important goal for the machine learning (ML) community is to create approaches that can learn solutions with human-level capability. One domain where humans have held a significant advantage is visual processing. A significant approach to addressing this gap has been machine learning approaches that are inspired from the natural systems, such as artificial neural networks (ANNs), evolutionary computation (EC), and generative and developmental systems (GDS). Research into deep learning has demonstrated that such architectures can achieve performance competitive with humans on some visual tasks; however, these systems have been primarily trained through supervised and unsupervised learning algorithms. Alternatively, research is showing that evolution may have a significant role in the development of visual systems. Thus this paper investigates the role neuro-evolution (NE) can take in deep learning. In particular, the Hypercube-based NeuroEvolution of Augmenting Topologies is a NE approach that can effectively learn large neural structures by training an indirect encoding that compresses the ANN weight pattern as a function of geometry. The results show that HyperNEAT struggles with performing image classification by itself, but can be effective in training a feature extractor that other ML approaches can learn from. Thus NeuroEvolution combined with other ML methods provides an intriguing area of research that can replicate the processes in nature.

Connected through hyperedge HE-1 (induced by source paper "optimization algorithm inspired deep neural network structure design"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "recurrent inference machines for solving inverse problems"
    - "practical block wise neural network architecture generation"
    - "convolutional neural fabrics"
    - "maximal sparsity with deep networks"
    - "deep convolutional neural networks with merge and run mappings"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "genesys enabling continuous learning through neural network evolution in hardware"):
  with members:
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "scaling deep learning on gpu and knights landing clusters"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "nmode neuro module evolution"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-3 (induced by source paper "towards co evolution of fitness predictors and deep neural networks"):
  with members:
    - "building high level features using large scale unsupervised learning"
    - "deep big simple neural nets for handwritten digit recognition"

Connected through hyperedge HE-4 (induced by source paper "analysis and optimization of convolutional neural network architectures"):
  with members:
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "convolutional neural fabrics"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "sgdr stochastic gradient descent with restarts"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "large scale evolution of image classifiers"):
  with members:
    - "convolutional neural fabrics"
    - "deep networks with stochastic depth"
    - "planet photo geolocation with convolutional neural networks"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "neural architecture search with reinforcement learning"
    - "convolution by evolution differentiable pattern producing networks"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-6 (induced by source paper "evolving deep convolutional neural networks for image classification"):
  with members:
    - "large scale evolution of image classifiers"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "neural architecture search with reinforcement learning"
    - "convolution by evolution differentiable pattern producing networks"
    - "designing neural network architectures using reinforcement learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "a genetic programming approach to designing convolutional neural network architectures"):
  with members:
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "densely connected convolutional networks"
    - "cma es for hyperparameter optimization of deep neural networks"
    - "colorful image colorization"
    - "neural architecture search with reinforcement learning"
    - "convolution by evolution differentiable pattern producing networks"
    - "designing neural network architectures using reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "accelerating neural architecture search using performance prediction"):
  with members:
    - "learning transferable architectures for scalable image recognition"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "smash one shot model architecture search through hypernetworks"
    - "neural architecture search with reinforcement learning"

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)