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: "adversarial dropout for supervised and semi supervised learning"
  Abstract: Recently, the training with adversarial examples, which are generated by adding a small but worst-case perturbation on input examples, has been proved to improve generalization performance of neural networks. In contrast to the individually biased inputs to enhance the generality, this paper introduces adversarial dropout, which is a minimal set of dropouts that maximize the divergence between the outputs from the network with the dropouts and the training supervisions. The identified adversarial dropout are used to reconfigure the neural network to train, and we demonstrated that training on the reconfigured sub-network improves the generalization performance of supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We analyzed the trained model to reason the performance improvement, and we found that adversarial dropout increases the sparsity of neural networks more than the standard dropout does.

Connected through hyperedge HE-1 (induced by source paper "two cycle learning clustering based regularisation for deep semi supervised classification"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "semi supervised learning by label gradient alignment"
    - "superpixel contracted graph based learning for hyperspectral image classification"
    - "beyond supervised classification extreme minimal supervision with the graph 1 laplacian"
    - "label propagation for deep semi supervised learning"
    - "deep clustering for unsupervised learning of visual features"
    - "deep metric transfer for label propagation with limited annotated data"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-2 (induced by source paper "self training with noisy student improves imagenet classification"):
  with members:
    - "using videos to evaluate image model robustness"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "semi supervised segmentation of salt bodies in seismic images using an ensemble of convolutional neural networks"
    - "robustness properties of facebook s resnext wsl models"
    - "billion scale semi supervised learning for image classification"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "natural adversarial examples"
    - "leveraging just a few keywords for fine grained aspect detection through weakly supervised co training"

Connected through hyperedge HE-3 (induced by source paper "unsupervised data augmentation for consistency training"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "mixture models for diverse machine translation tricks of the trade"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-4 (induced by source paper "d textbf s 3 l deep self semi supervised learning for image recognition"):
  with members:
    - "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods"
    - "s4l self supervised semi supervised learning"
    - "a meta approach to defend noisy labels by the manifold regularizer psdr"
    - "unsupervised label noise modeling and loss correction"
    - "pushing the right boundaries matters wasserstein adversarial training for label noise"
    - "weight standardization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-5 (induced by source paper "exploring self supervised regularization for supervised and semi supervised learning"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "continual lifelong learning with neural networks a review"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "bridging theory and algorithm for domain adaptation"
    - "label propagation for deep semi supervised learning"
    - "mixmatch a holistic approach to semi supervised learning"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"

Connected through hyperedge HE-6 (induced by source paper "countering noisy labels by learning from auxiliary clean labels"):
  with members:
    - "a meta approach to defend noisy labels by the manifold regularizer psdr"
    - "unsupervised label noise modeling and loss correction"
    - "pushing the right boundaries matters wasserstein adversarial training for label noise"
    - "weight standardization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "revisiting self supervised visual representation learning"
    - "high fidelity image generation with fewer labels"
    - "group normalization"

Connected through hyperedge HE-7 (induced by source paper "drop to adapt learning discriminative features for unsupervised domain adaptation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "co regularized alignment for unsupervised domain adaptation"
    - "multi adversarial domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "unsupervised domain adaptation with similarity learning"
    - "adversarial discriminative domain adaptation"
    - "self ensembling for visual domain adaptation"

Connected through hyperedge HE-8 (induced by source paper "graph adversarial training dynamically regularizing based on graph structure"):
  with members:
    - "neural graph collaborative filtering"
    - "adversarial attack on graph structured data"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adversarial training towards robust multimedia recommender system"
    - "aaane attention based adversarial autoencoder for multi scale network embedding"
    - "hierarchical graph representation learning with differentiable pooling"
    - "adversarial personalized ranking for recommendation"
    - "deep learning on graphs a survey"

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)