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: "the shallow end empowering shallower deep convolutional networks through auxiliary outputs"
  Abstract: Depth is one of the key factors behind the success of convolutional neural networks (CNNs). Since ResNet, we are able to train very deep CNNs as the gradient vanishing issue has been largely addressed by the introduction of skip connections. However, we observe that, when the depth is very large, the intermediate layers (especially shallow layers) may fail to receive sufficient supervision from the loss due to the severe transformation through a long backpropagation path. As a result, the representation power of intermediate layers can be very weak and the model becomes very redundant with limited performance. In this paper, we first investigate the supervision vanishing issue in existing backpropagation (BP) methods. And then, we propose to address it via an effective method, called Multi-way BP (MW-BP), which relies on multiple auxiliary losses added to the intermediate layers of the network. The proposed MW-BP method can be applied to most deep architectures with slight modifications, such as ResNet and MobileNet. Our method often gives rise to much more compact models (denoted by "Mw+Architecture") than existing methods. For example, MwResNet-44 with 44 layers performs better than ResNet-110 with 110 layers on CIFAR-10 and CIFAR-100. More critically, the resultant models even outperform the light models obtained by state-of-the-art model compression methods. Last, our method inherently produces multiple compact models with different depths at the same time, which is helpful for model selection.

Connected through hyperedge HE-1 (induced by source paper "discrimination aware network pruning for deep model compression"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "towards optimal structured cnn pruning via generative adversarial learning"
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "importance estimation for neural network pruning"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-2 (induced by source paper "closed loop matters dual regression networks for single image super resolution"):
  with members:
    - "intelligent home 3d automatic 3d house design from linguistic descriptions only"
    - "hierarchical neural architecture search for single image super resolution"
    - "discrimination aware network pruning for deep model compression"
    - "multi marginal wasserstein gan"
    - "collaborative unsupervised domain adaptation for medical image diagnosis"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "ranksrgan generative adversarial networks with ranker for image super resolution"
    - "nat neural architecture transformer for accurate and compact architectures"

Connected through hyperedge HE-3 (induced by source paper "hierarchical neural architecture search for single image super resolution"):
  with members:
    - "discrimination aware network pruning for deep model compression"
    - "fast accurate and lightweight super resolution with neural architecture search"
    - "lightweight feature fusion network for single image super resolution"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "adversarial learning with local coordinate coding"
    - "fast accurate and lightweight super resolution with cascading residual network"

Connected through hyperedge HE-4 (induced by source paper "nat neural architecture transformer for accurate and compact architectures"):
  with members:
    - "multi marginal wasserstein gan"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "the evolved transformer"
    - "learnable embedding space for efficient neural architecture compression"
    - "graph convolutional networks for temporal action localization"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "adversarial learning with local coordinate coding"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-5 (induced by source paper "discrimination aware channel pruning for deep neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "sphereface deep hypersphere embedding for face recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "towards effective low bitwidth convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "elastic neural networks for classification"):
  with members:
    - "elastic neural networks a scalable framework for embedded computer vision"
    - "shakedrop regularization for deep residual learning"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"

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)