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: "learning strict identity mappings in deep residual networks"
  Abstract: A family of super deep networks, referred to as residual networks or ResNet, achieved record-beating performance in various visual tasks such as image recognition, object detection, and semantic segmentation. The ability to train very deep networks naturally pushed the researchers to use enormous resources to achieve the best performance. Consequently, in many applications super deep residual networks were employed for just a marginal improvement in performance. In this paper, we propose epsilon-ResNet that allows us to automatically discard redundant layers, which produces responses that are smaller than a threshold epsilon, with a marginal or no loss in performance. The epsilon-ResNet architecture can be achieved using a few additional rectified linear units in the original ResNet. Our method does not use any additional variables nor numerous trials like other hyper-parameter optimization techniques. The layer selection is achieved using a single training process and the evaluation is performed on CIFAR-10, CIFAR-100, SVHN, and ImageNet datasets. In some instances, we achieve about 80% reduction in the number of parameters.

Connected through hyperedge HE-1 (induced by source paper "lossless compression of deep neural networks"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "caql continuous action q learning"
    - "function approximation by deep networks"
    - "equivalent and approximate transformations of deep neural networks"
    - "differentiable convex optimization layers"
    - "accelerating primal solution findings for mixed integer programs based on solution prediction"
    - "reinforcement learning for integer programming learning to cut"
    - "machine learning for combinatorial optimization a methodological tour d horizon"

Connected through hyperedge HE-2 (induced by source paper "equivalent and approximate transformations of deep neural networks"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "empirical bounds on linear regions of deep rectifier networks"
    - "deep networks with probabilistic gates"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "maximum resilience of artificial neural networks"

Connected through hyperedge HE-3 (induced by source paper "network implosion effective model compression for resnets via static layer pruning and retraining"):
  with members:
    - "data driven sparse structure selection for deep neural networks"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "convolutional sequence to sequence learning"
    - "adaptive learning rate via covariance matrix based preconditioning for deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "identity mappings in deep residual networks"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "towards understanding the importance of shortcut connections in residual networks"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"
    - "fine tuned language models for text classification"
    - "universal language model fine tuning for text classification"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "visualizing the loss landscape of neural nets"
    - "super convergence very fast training of residual networks using large learning rates"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-5 (induced by source paper "knowledge representing efficient sparse representation of prior knowledge for knowledge distillation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "data driven sparse structure selection for deep neural networks"
    - "a geometric view of optimal transportation and generative model"
    - "thinet a filter level pruning method for deep neural network compression"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "a survey of model compression and acceleration for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep model compression via deep reinforcement learning"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "droppruning for model compression"
    - "discrimination aware channel pruning for deep neural networks"
    - "haq hardware aware automated quantization with mixed precision"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-7 (induced by source paper "a cnn based methodology for breast cancer diagnosis using thermal images"):
  with members:
    - "squeeze and excitation networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "xception deep learning with depthwise separable convolutions"

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)