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: "exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search"
  Abstract: Researchers have applied deep neural networks to image restoration tasks, in which they proposed various network architectures, loss functions, and training methods. In particular, adversarial training, which is employed in recent studies, seems to be a key ingredient to success. In this paper, we show that simple convolutional autoencoders (CAEs) built upon only standard network components, i.e., convolutional layers and skip connections, can outperform the state-of-the-art methods which employ adversarial training and sophisticated loss functions. The secret is to employ an evolutionary algorithm to automatically search for good architectures. Training optimized CAEs by minimizing the $\ell_2$ loss between reconstructed images and their ground truths using the ADAM optimizer is all we need. Our experimental results show that this approach achieves 27.8 dB peak signal to noise ratio (PSNR) on the CelebA dataset and 40.4 dB on the SVHN dataset, compared to 22.8 dB and 33.0 dB provided by the former state-of-the-art methods, respectively.

Connected through hyperedge HE-1 (induced by source paper "memory efficient hierarchical neural architecture search for image denoising"):
  with members:
    - "generating training data for denoising real rgb images via camera pipeline simulation"
    - "nas fcos fast neural architecture search for object detection"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "darts improved differentiable architecture search with early stopping"
    - "dual residual networks leveraging the potential of paired operations for image restoration"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "graph hypernetworks for neural architecture search"
    - "toward convolutional blind denoising of real photographs"

Connected through hyperedge HE-2 (induced by source paper "scalable reinforcement learning based neural architecture search for cancer deep learning research"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "evolutionary neural automl for deep learning"
    - "evaluating the search phase of neural architecture search"
    - "balsam automated scheduling and execution of dynamic data intensive hpc workflows"
    - "evolutionary neural architecture search for image restoration"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"

Connected through hyperedge HE-3 (induced by source paper "restoring images with unknown degradation factors by recurrent use of a multi branch network"):
  with members:
    - "dual residual networks leveraging the potential of paired operations for image restoration"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "image super resolution using very deep residual channel attention networks"
    - "competitive inner imaging squeeze and excitation for residual network"
    - "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "crafting a toolchain for image restoration by deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "dual residual networks leveraging the potential of paired operations for image restoration"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "gated fusion network for single image dehazing"
    - "yolov3 an incremental improvement"
    - "recurrent squeeze and excitation context aggregation net for single image deraining"
    - "real world noisy image denoising a new benchmark"
    - "non locally enhanced encoder decoder network for single image de raining"
    - "density aware single image de raining using a multi stream dense network"
    - "deep back projection networks for super resolution"

Connected through hyperedge HE-5 (induced by source paper "blind universal bayesian image denoising with gaussian noise level learning"):
  with members:
    - "class aware fully convolutional gaussian and poisson denoising"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "taskonomy disentangling task transfer learning"
    - "unsupervised discovery of object landmarks as structural representations"
    - "deep learning for image denoising a survey"
    - "unprocessing images for learned raw denoising"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "learning a single convolutional super resolution network for multiple degradations"

Connected through hyperedge HE-6 (induced by source paper "best practices for scientific research on neural architecture search"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "autogan neural architecture search for generative adversarial networks"
    - "the evolved transformer"
    - "nas bench 101 towards reproducible neural architecture search"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "evaluating the search phase of neural architecture search"
    - "autodispnet improving disparity estimation with automl"
    - "tabular benchmarks for joint architecture and hyperparameter optimization"

Connected through hyperedge HE-7 (induced by source paper "adaptive stochastic natural gradient method for one shot neural architecture search"):
  with members:
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "dynamic optimization of neural network structures using probabilistic modeling"
    - "neural architecture optimization"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"
    - "efficient neural architecture search via parameter sharing"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-8 (induced by source paper "efficient residual dense block search for image super resolution"):
  with members:
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "darts differentiable architecture search"

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)