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: "diracnets training very deep neural networks without skip connections"
  Abstract: Deep neural networks with skip-connections, such as ResNet, show excellent performance in various image classification benchmarks. It is though observed that the initial motivation behind them - training deeper networks - does not actually hold true, and the benefits come from increased capacity, rather than from depth. Motivated by this, and inspired from ResNet, we propose a simple Dirac weight parameterization, which allows us to train very deep plain networks without explicit skip-connections, and achieve nearly the same performance. This parameterization has a minor computational cost at training time and no cost at all at inference, as both Dirac parameterization and batch normalization can be folded into convolutional filters, so that network becomes a simple chain of convolution-ReLU pairs. We are able to match ResNet-1001 accuracy on CIFAR-10 with 28-layer wider plain DiracNet, and closely match ResNets on ImageNet. Our parameterization also mostly eliminates the need of careful initialization in residual and non-residual networks. The code and models for our experiments are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "deep learning for image denoising a survey"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "group normalization"
    - "a variational u net for conditional appearance and shape generation"
    - "residual dense network for image super resolution"
    - "correction by projection denoising images with generative adversarial networks"
    - "dilated deep residual network for image denoising"

Connected through hyperedge HE-2 (induced by source paper "ffdnet toward a fast and flexible solution for cnn based image denoising"):
  with members:
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning non local image diffusion for image denoising"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "benchmarking denoising algorithms with real photographs"
    - "memnet a persistent memory network for image restoration"
    - "svdnet for pedestrian retrieval"

Connected through hyperedge HE-3 (induced by source paper "neural proximal gradient descent for compressive imaging"):
  with members:
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "searching for activation functions"
    - "unrolled optimization with deep priors"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "perceptual losses for real time style transfer and super resolution"
    - "maximal sparsity with deep networks"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-4 (induced by source paper "the exploding gradient problem demystified definition prevalence impact origin tradeoffs and solutions"):
  with members:
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "training and inference with integers in deep neural networks"
    - "stable architectures for deep neural networks"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "learning across scales a multiscale method for convolution neural networks"
    - "the shattered gradients problem if resnets are the answer then what is the question"

Connected through hyperedge HE-5 (induced by source paper "eraserelu a simple way to ease the training of deep convolution neural networks"):
  with members:
    - "detecting and recognizing human object interactions"
    - "rethink relu to training better cnns"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "more is less a more complicated network with less inference complexity"
    - "shake shake regularization"
    - "convolutional pose machines"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-6 (induced by source paper "beyond finite layer neural networks bridging deep architectures and numerical differential equations"):
  with members:
    - "the reversible residual network backpropagation without storing activations"
    - "deep residual learning and pdes on manifold"
    - "squeeze and excitation networks"
    - "opening the black box of deep neural networks via information"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "shake shake regularization"
    - "residual networks are exponential ensembles of relatively shallow networks"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-7 (induced by source paper "the shattered gradients problem if resnets are the answer then what is the question"):
  with members:
    - "skip connections as effective symmetry breaking"
    - "deep online convex optimization with gated games"
    - "neural taylor approximations convergence and exploration in rectifier networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "benefits of depth in neural networks"
    - "residual networks behave like ensembles of relatively shallow networks"
    - "highway and residual networks learn unrolled iterative estimation"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"

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.CV(Computer Vision and Pattern Recognition)