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: "improved dropout for shallow and deep learning"
  Abstract: Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic regression. However, the independent sampling for dropout could be suboptimal for the sake of convergence. In this paper, we propose to use multinomial sampling for dropout, i.e., sampling features or neurons according to a multinomial distribution with different probabilities for different features/neurons. To exhibit the optimal dropout probabilities, we analyze the shallow learning with multinomial dropout and establish the risk bound for stochastic optimization. By minimizing a sampling dependent factor in the risk bound, we obtain a distribution-dependent dropout with sampling probabilities dependent on the second order statistics of the data distribution. To tackle the issue of evolving distribution of neurons in deep learning, we propose an efficient adaptive dropout (named \textbf{evolutional dropout}) that computes the sampling probabilities on-the-fly from a mini-batch of examples. Empirical studies on several benchmark datasets demonstrate that the proposed dropouts achieve not only much faster convergence and but also a smaller testing error than the standard dropout. For example, on the CIFAR-100 data, the evolutional dropout achieves relative improvements over 10\% on the prediction performance and over 50\% on the convergence speed compared to the standard dropout.

Connected through hyperedge HE-1 (induced by source paper "dropout explicit forms and capacity control"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "on the implicit bias of dropout"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "brain tumor segmentation with deep neural networks"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"

Connected through hyperedge HE-2 (induced by source paper "shakeout a new approach to regularized deep neural network training"):
  with members:
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "hcp a flexible cnn framework for multi label image classification"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "sparse activity and sparse connectivity in supervised learning"
    - "wide residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-3 (induced by source paper "survey of dropout methods for deep neural networks"):
  with members:
    - "effective and efficient dropout for deep convolutional neural networks"
    - "adversarial dropout for recurrent neural networks"
    - "ising dropout a regularization method for training and compression of deep neural networks"
    - "recent advances in recurrent neural networks"
    - "on the state of the art of evaluation in neural language models"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "regularizing and optimizing lstm language models"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-4 (induced by source paper "regularizing neural networks via stochastic branch layers"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep networks with stochastic depth"
    - "dropout with expectation linear regularization"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "chd consecutive horizontal dropout for human gait feature extraction"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "horizontal pyramid matching for person re identification"
    - "gaitset regarding gait as a set for cross view gait recognition"
    - "in defense of the triplet loss for person re identification"
    - "learning deep context aware features over body and latent parts for person re identification"
    - "towards dropout training for convolutional neural networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "pseudo task augmentation from deep multitask learning to intratask sharing and back"):
  with members:
    - "evolving deep neural networks"
    - "learned in translation contextualized word vectors"
    - "learning multiple visual domains with residual adapters"
    - "pathnet evolution channels gradient descent in super neural networks"
    - "population based training of neural networks"
    - "distral robust multitask reinforcement learning"
    - "an overview of multi task learning in deep neural networks"
    - "one model to learn them all"

Connected through hyperedge HE-7 (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"
    - "shakeout a new approach to regularized deep neural network training"
    - "recovering from random pruning on the plasticity of deep convolutional neural networks"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "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-8 (induced by source paper "regularizing deep neural networks by noise its interpretation and optimization"):
  with members:
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "dropout with expectation linear regularization"
    - "convolutional two stream network fusion for video action recognition"
    - "variational inference for monte carlo objectives"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "deep pyramidal residual 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.LG(Machine Learning)