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: "understanding data augmentation for classification when to warp"
  Abstract: In this paper we investigate the benefit of augmenting data with synthetically created samples when training a machine learning classifier. Two approaches for creating additional training samples are data warping, which generates additional samples through transformations applied in the data-space, and synthetic over-sampling, which creates additional samples in feature-space. We experimentally evaluate the benefits of data augmentation for a convolutional backpropagation-trained neural network, a convolutional support vector machine and a convolutional extreme learning machine classifier, using the standard MNIST handwritten digit dataset. We found that while it is possible to perform generic augmentation in feature-space, if plausible transforms for the data are known then augmentation in data-space provides a greater benefit for improving performance and reducing overfitting.

Connected through hyperedge HE-1 (induced by source paper "do cnns encode data augmentations"):
  with members:
    - "how much position information do convolutional neural networks encode"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial examples improve image recognition"
    - "mixmatch a holistic approach to semi supervised learning"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "self training with noisy student improves imagenet classification"
    - "regularized evolution for image classifier architecture search"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-2 (induced by source paper "addressing the confounds of accompaniments in singer identification"):
  with members:
    - "score and lyrics free singing voice generation"
    - "dilated convolution with dilated gru for music source separation"
    - "learning a joint embedding space of monophonic and mixed music signals for singing voice"
    - "an overview of lead and accompaniment separation in music"
    - "denoising auto encoder with recurrent skip connections and residual regression for music source separation"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "towards a resilient machine learning classifier a case study of ransomware detection"):
  with members:
    - "an introduction to variational autoencoders"
    - "towards resilient machine learning for ransomware detection"
    - "sanity checks for saliency maps"
    - "axiomatic attribution for deep networks"
    - "automated dynamic analysis of ransomware benefits limitations and use for detection"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-4 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges"):
  with members:
    - "example and feature importance based explanations for black box machine learning models"
    - "back to basics benchmarking canonical evolution strategies for playing atari"
    - "visual interpretability for deep learning a survey"
    - "controlling over generalization and its effect on adversarial examples detection and generation"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "counterexample guided data augmentation"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"

Connected through hyperedge HE-6 (induced by source paper "techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "autoaugment learning augmentation policies from data"
    - "deep forest"

Connected through hyperedge HE-7 (induced by source paper "general probabilistic surface optimization and log density estimation"):
  with members:
    - "deep pdf probabilistic surface optimization and density estimation"
    - "spectral normalization for generative adversarial networks"
    - "understanding the effectiveness of lipschitz continuity in generative adversarial nets"
    - "mine mutual information neural estimation"
    - "began boundary equilibrium generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "population based augmentation efficient learning of augmentation policy schedules"):
  with members:
    - "data augmentation with manifold exploring geometric transformations for increased performance and robustness"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "data augmentation by pairing samples for images classification"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "autoaugment learning augmentation policies from data"
    - "efficient neural architecture search via parameter sharing"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "learning transferable architectures for scalable image recognition"

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.CV(Computer Vision and Pattern Recognition)