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: "dsd regularizing deep neural networks with dense sparse dense training flow"
  Abstract: Modern deep neural networks have a large number of parameters, making them very powerful machine learning systems. A critical issue for training such large networks on large-scale data-sets is to prevent overfitting while at the same time providing enough model capacity. We propose DSD, a dense-sparse-dense training flow, for regularizing deep neural networks. In the first D step, we train a dense network to learn which connections are important. In the S step, we regularize the network by pruning the unimportant connections and retrain the network given the sparsity constraint. In the final D step, we increase the model capacity by freeing the sparsity constraint, re-initializing the pruned parameters, and retraining the whole dense network. Experiments show that DSD training can improve the performance of a wide range of CNN, RNN and LSTMs on the tasks of image classification, caption generation and speech recognition. On the Imagenet dataset, DSD improved the absolute accuracy of AlexNet, GoogleNet, VGG-16, ResNet50, ResNet-152 and SqueezeNet by a geo-mean of 2.1 points (Top-1) and 1.4 points (Top-5). On the WSJ’92 and WSJ’93 dataset, DSD improved DeepSpeech2 WER by 0.53 and 1.08 points. On the Flickr-8K dataset, DSD improved the NeuralTalk BLEU score by 2.0 points. DSD training flow produces the same model architecture and doesn’t incur any inference overhead.

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 "training simplification and model simplification for deep learning a minimal effort back propagation method"):
  with members:
    - "bag of words as target for neural machine translation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "nonparametric neural networks"
    - "sparse communication for distributed gradient descent"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "leanconvnets low cost yet effective convolutional neural networks"):
  with members:
    - "imexnet a forward stable deep neural network"
    - "deep learning for audio signal processing"
    - "mixconv mixed depthwise convolutional kernels"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "scalable methods for 8 bit training of neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "diagonalwise refactorization an efficient training method for depthwise convolutions"

Connected through hyperedge HE-5 (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-6 (induced by source paper "equivalent and approximate transformations of deep neural networks"):
  with members:
    - "quantized densely connected u nets for efficient landmark localization"
    - "learning strict identity mappings in deep residual networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "leanresnet a low cost yet effective convolutional residual networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "diagonalwise refactorization an efficient training method for depthwise convolutions"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "neural ordinary differential equations"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "importance estimation for neural network pruning"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "progressive deep neural networks acceleration via soft filter pruning"
    - "rethinking the value of network pruning"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "data driven sparse structure selection for deep neural networks"

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)