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: "shakeout a new approach to regularized deep neural network training"
  Abstract: Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines $L_0$, $L_1$ and $L_2$ regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.

Connected through hyperedge HE-1 (induced by source paper "asymptotic soft filter pruning for deep convolutional neural networks"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "learning to prune filters in convolutional neural networks"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "discrimination aware channel pruning for deep neural networks"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "coreset based neural network compression"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-2 (induced by source paper "contrastive adaptation network for unsupervised domain adaptation"):
  with members:
    - "searching for a robust neural architecture in four gpu hours"
    - "visual domain adaptation with manifold embedded distribution alignment"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "multi adversarial domain adaptation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "in defense of the triplet loss for person re identification"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"

Connected through hyperedge HE-3 (induced by source paper "mpdcompress matrix permutation decomposition algorithm for deep neural network compression"):
  with members:
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deeppicar a low cost deep neural network based autonomous car"
    - "structured deep neural network pruning via matrix pivoting"
    - "sep nets small and effective pattern networks"
    - "ese efficient speech recognition engine with sparse lstm on fpga"

Connected through hyperedge HE-4 (induced by source paper "soft filter pruning for accelerating deep convolutional neural networks"):
  with members:
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "thinet a filter level pruning method for deep neural network compression"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "more is less a more complicated network with less inference complexity"
    - "channel pruning for accelerating very deep neural networks"
    - "learning structured sparsity in deep neural networks"
    - "identity mappings in deep residual networks"
    - "trained ternary quantization"

Connected through hyperedge HE-5 (induced by source paper "excitation dropout encouraging plasticity in deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "improved dropout for shallow and deep learning"
    - "top down neural attention by excitation backprop"
    - "towards dropout training for convolutional neural networks"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "dropout with tabu strategy for regularizing deep neural networks"):
  with members:
    - "self normalizing neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "deep learning in neural networks"

Connected through hyperedge HE-7 (induced by source paper "swgridnet a deep convolutional neural network based on grid topology for image classification"):
  with members:
    - "residual conv deconv grid network for semantic segmentation"
    - "improved regularization of convolutional neural networks with cutout"
    - "shake shake regularization"
    - "sgdr stochastic gradient descent with restarts"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "understanding deep learning requires rethinking generalization"
    - "densely connected convolutional 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)