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: "dropout rademacher complexity of deep neural networks"
  Abstract: Great successes of deep neural networks have been witnessed in various real applications. Many algorithmic and implementation techniques have been developed, however, theoretical understanding of many aspects of deep neural networks is far from clear. A particular interesting issue is the usefulness of dropout, which was motivated from the intuition of preventing complex co-adaptation of feature detectors. In this paper, we study the Rademacher complexity of different types of dropout, and our theoretical results disclose that for shallow neural networks (with one or none hidden layer) dropout is able to reduce the Rademacher complexity in polynomial, whereas for deep neural networks it can amazingly lead to an exponential reduction of the Rademacher complexity.

Connected through hyperedge HE-1 (induced by source paper "a theory of usable information under computational constraints"):
  with members:
    - "understanding the limitations of variational mutual information estimators"
    - "on variational bounds of mutual information"
    - "learning adversarially fair and transferable representations"
    - "learning controllable fair representations"
    - "representation learning with contrastive predictive coding"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "estimating mutual information for discrete continuous mixtures"
    - "censoring representations with an adversary"

Connected through hyperedge HE-2 (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-3 (induced by source paper "on dropout and nuclear norm regularization"):
  with members:
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "on the implicit bias of dropout"
    - "gradient descent aligns the layers of deep linear networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "a comprehensive analysis of deep regression"

Connected through hyperedge HE-4 (induced by source paper "dual branch residual network for lung nodule segmentation"):
  with members:
    - "residual dense network for image super resolution"
    - "brain tumor segmentation with deep neural networks"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "densely connected convolutional networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "automatic segmentation of mr brain images with a convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "data interpolating prediction alternative interpretation of mixup"):
  with members:
    - "data augmentation by pairing samples for images classification"
    - "mixup as locally linear out of manifold regularization"
    - "improving deep learning using generic data augmentation"
    - "learning from between class examples for deep sound recognition"
    - "mixup beyond empirical risk minimization"
    - "between class learning for image classification"
    - "identity mappings in deep residual networks"
    - "importance weighted autoencoders"

Connected through hyperedge HE-6 (induced by source paper "dating ancient paintings of mogao grottoes using deeply learnt visual codes"):
  with members:
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "dropout with expectation linear regularization"):
  with members:
    - "surprising properties of dropout in deep networks"
    - "deep learning in neural networks"
    - "to drop or not to drop robustness consistency and differential privacy properties of dropout"
    - "striving for simplicity the all convolutional net"
    - "on the inductive bias of dropout"
    - "dropout training for support vector machines"
    - "improving neural networks by preventing co adaptation of feature detectors"

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.NE(Neural and Evolutionary Computing)