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: "surprising properties of dropout in deep networks"
  Abstract: We analyze dropout in deep networks with rectified linear units and the quadratic loss. Our results expose surprising differences between the behavior of dropout and more traditional regularizers like weight decay. For example, on some simple data sets dropout training produces negative weights even though the output is the sum of the inputs. This provides a counterpoint to the suggestion that dropout discourages co-adaptation of weights. We also show that the dropout penalty can grow exponentially in the depth of the network while the weight-decay penalty remains essentially linear, and that dropout is insensitive to various re-scalings of the input features, outputs, and network weights. This last insensitivity implies that there are no isolated local minima of the dropout training criterion. Our work uncovers new properties of dropout, extends our understanding of why dropout succeeds, and lays the foundation for further progress.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"

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 "breaking inter layer co adaptation by classifier anonymization"):
  with members:
    - "deep networks with stochastic depth"
    - "analysis of dropout learning regarded as ensemble learning"
    - "swapout learning an ensemble of deep architectures"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "on the inductive bias of dropout"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-5 (induced by source paper "on approximating dropout noise injection"):
  with members:
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "on the inductive bias of dropout"

Connected through hyperedge HE-6 (induced by source paper "data augmentation instead of explicit regularization"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "further advantages of data augmentation on convolutional neural networks"
    - "mixup as locally linear out of manifold regularization"
    - "do deep nets really need weight decay and dropout"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "spectrally normalized margin bounds for neural networks"
    - "improved regularization of convolutional neural networks with cutout"

Connected through hyperedge HE-7 (induced by source paper "dropout with expectation linear regularization"):
  with members:
    - "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 rademacher complexity of deep neural networks"
    - "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.LG(Machine Learning)