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 training for support vector machines"
  Abstract: Dropout and other feature noising schemes have shown promising results in controlling over-fitting by artificially corrupting the training data. Though extensive theoretical and empirical studies have been performed for generalized linear models, little work has been done for support vector machines (SVMs), one of the most successful approaches for supervised learning. This paper presents dropout training for linear SVMs. To deal with the intractable expectation of the non-smooth hinge loss under corrupting distributions, we develop an iteratively re-weighted least square (IRLS) algorithm by exploring data augmentation techniques. Our algorithm iteratively minimizes the expectation of a re-weighted least square problem, where the re-weights have closed-form solutions. The similar ideas are applied to develop a new IRLS algorithm for the expected logistic loss under corrupting distributions. Our algorithms offer insights on the connection and difference between the hinge loss and logistic loss in dropout training. Empirical results on several real datasets demonstrate the effectiveness of dropout training on significantly boosting the classification accuracy of linear SVMs.

Connected through hyperedge HE-1 (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"
    - "improved dropout for shallow and deep learning"
    - "wide residual networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-2 (induced by source paper "disturbance grassmann kernels for subspace based learning"):
  with members:
    - "dictionary learning and sparse coding on grassmann manifolds an extrinsic solution"

Connected through hyperedge HE-3 (induced by source paper "on the origin of deep learning"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "lstm a search space odyssey"
    - "an analysis of deep neural network models for practical applications"
    - "calibrating energy based generative adversarial networks"
    - "harnessing deep neural networks with logic rules"
    - "nips 2016 tutorial generative adversarial networks"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-4 (induced by source paper "adversarial dropout for supervised and semi supervised learning"):
  with members:
    - "identity mappings in deep residual networks"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "densely connected convolutional networks"
    - "improved dropout for shallow and deep learning"
    - "adversarial examples in the physical world"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "improved dropout for shallow and deep learning"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "efficient batchwise dropout training using submatrices"
    - "path sgd path normalized optimization in deep neural networks"
    - "adam a method for stochastic optimization"
    - "deep learning with elastic averaging sgd"
    - "on the inductive bias of dropout"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-6 (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 rademacher complexity of deep neural networks"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-7 (induced by source paper "dropout training for svms with data augmentation"):
  with members:
    - "a bayesian encourages dropout"
    - "on the inductive bias of dropout"
    - "efficient gradient based inference through transformations between bayes nets and neural nets"
    - "representation learning a review and new perspectives"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "marginalized denoising autoencoders for domain adaptation"
    - "mean field theory for sigmoid belief 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)