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: "training skinny deep neural networks with iterative hard thresholding methods"
  Abstract: Deep neural networks have achieved remarkable success in a wide range of practical problems. However, due to the inherent large parameter space, deep models are notoriously prone to overfitting and difficult to be deployed in portable devices with limited memory. In this paper, we propose an iterative hard thresholding (IHT) approach to train Skinny Deep Neural Networks (SDNNs). An SDNN has much fewer parameters yet can achieve competitive or even better performance than its full CNN counterpart. More concretely, the IHT approach trains an SDNN through following two alternative phases: (I) perform hard thresholding to drop connections with small activations and fine-tune the other significant filters; (II)~re-activate the frozen connections and train the entire network to improve its overall discriminative capability. We verify the superiority of SDNNs in terms of efficiency and classification performance on four benchmark object recognition datasets, including CIFAR-10, CIFAR-100, MNIST and ImageNet. Experimental results clearly demonstrate that IHT can be applied for training SDNN based on various CNN architectures such as NIN and AlexNet.

Connected through hyperedge HE-1 (induced by source paper "deep sparse band selection for hyperspectral face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "attribute guided coupled gan for cross resolution face recognition"
    - "adversarial examples to fool iris recognition systems"
    - "defending against adversarial iris examples using wavelet decomposition"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "object detection with deep learning a review"
    - "multibiometric secure system based on deep learning"

Connected through hyperedge HE-2 (induced by source paper "localization aware channel pruning for object detection"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "focused quantization for sparse cnns"
    - "mask scoring r cnn"
    - "objects as points"
    - "play and prune adaptive filter pruning for deep model compression"
    - "cornernet detecting objects as paired keypoints"
    - "efficient neural network compression"

Connected through hyperedge HE-3 (induced by source paper "scann synthesis of compact and accurate neural networks"):
  with members:
    - "hardware guided symbiotic training for compact accurate yet execution efficient lstm"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "grow and prune compact fast and accurate lstms"
    - "adam admm a unified systematic framework of structured weight pruning for dnns"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-4 (induced by source paper "light multi segment activation for model compression"):
  with members:
    - "model compression via distillation and quantization"
    - "private model compression via knowledge distillation"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "wrpn wide reduced precision networks"
    - "attention is all you need"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "ternary neural networks with fine grained quantization"

Connected through hyperedge HE-5 (induced by source paper "120001 0 regularized structured sparsity convolutional neural networks"):
  with members:
    - "transformed ell_1 regularization for learning sparse deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "discrimination aware channel pruning for deep neural networks"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "learning structured sparsity in deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "the lottery ticket hypothesis finding sparse trainable neural networks"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "rethinking the value of network pruning"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep rewiring training very sparse deep networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning compact neural networks with regularization"):
  with members:
    - "end to end learning of a convolutional neural network via deep tensor decomposition"
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"

Connected through hyperedge HE-8 (induced by source paper "task specific deep lda pruning of neural networks"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "channel pruning for accelerating very deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"
    - "pruning filters for efficient convnets"

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)