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: "improving robustness without sacrificing accuracy with patch gaussian augmentation"
  Abstract: Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions. While architectural advances have led to improved accuracy, building robust models remains challenging. Prior work has argued that there is an inherent trade-off between robustness and accuracy, which is exemplified by standard data augment techniques such as Cutout, which improves clean accuracy but not robustness, and additive Gaussian noise, which improves robustness but hurts accuracy. To overcome this trade-off, we introduce Patch Gaussian, a simple augmentation scheme that adds noise to randomly selected patches in an input image. Models trained with Patch Gaussian achieve state of the art on the CIFAR-10 and ImageNetCommon Corruptions benchmarks while also improving accuracy on clean data. We find that this augmentation leads to reduced sensitivity to high frequency noise(similar to Gaussian) while retaining the ability to take advantage of relevant high frequency information in the image (similar to Cutout). Finally, we show that Patch Gaussian can be used in conjunction with other regularization methods and data augmentation policies such as AutoAugment, and improves performance on the COCO object detection benchmark.

Connected through hyperedge HE-1 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation"):
  with members:
    - "fixmatch simplifying semi supervised learning with consistency and confidence"
    - "faster autoaugment learning augmentation strategies using backpropagation"
    - "starnet targeted computation for object detection in point clouds"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "efficientdet scalable and efficient object detection"
    - "randaugment practical automated data augmentation with a reduced search space"

Connected through hyperedge HE-2 (induced by source paper "increasing the robustness of dnns against image corruptions by playing the game of noise"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "mnist c a robustness benchmark for computer vision"
    - "adversarial examples are a natural consequence of test error in noise"
    - "certified adversarial robustness via randomized smoothing"
    - "transfer of adversarial robustness between perturbation types"
    - "making convolutional networks shift invariant again"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial training for free"

Connected through hyperedge HE-3 (induced by source paper "self training with noisy student improves imagenet classification"):
  with members:
    - "using videos to evaluate image model robustness"
    - "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"
    - "randaugment practical automated data augmentation with a reduced search space"

Connected through hyperedge HE-4 (induced by source paper "learning data augmentation strategies for object detection"):
  with members:
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "fast autoaugment"
    - "a fourier perspective on model robustness in computer vision"
    - "metaanchor learning to detect objects with customized anchors"
    - "dropblock a regularization method for convolutional networks"
    - "regularized evolution for image classifier architecture search"
    - "autoaugment learning augmentation policies from data"

Connected through hyperedge HE-5 (induced by source paper "randaugment practical automated data augmentation with a reduced search space"):
  with members:
    - "starnet targeted computation for object detection in point clouds"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "classification accuracy score for conditional generative models"
    - "instaboost boosting instance segmentation via probability map guided copy pasting"
    - "fast autoaugment"
    - "do imagenet classifiers generalize to imagenet"
    - "unsupervised data augmentation for consistency training"

Connected through hyperedge HE-6 (induced by source paper "randaugment practical data augmentation with no separate search"):
  with members:
    - "starnet targeted computation for object detection in point clouds"
    - "population based augmentation efficient learning of augmentation policy schedules"
    - "adversarial examples are a natural consequence of test error in noise"
    - "classification accuracy score for conditional generative models"
    - "do imagenet classifiers generalize to imagenet"
    - "a fourier perspective on model robustness in computer vision"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning data augmentation strategies for object detection"

Connected through hyperedge HE-7 (induced by source paper "adversarial examples improve image recognition"):
  with members:
    - "natural adversarial examples"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "searching for mobilenetv3"
    - "theoretically principled trade off between robustness and accuracy"
    - "fast autoaugment"
    - "interpreting adversarially trained convolutional neural networks"
    - "a fourier perspective on model robustness in computer vision"

Connected through hyperedge HE-8 (induced by source paper "ceb improves model robustness"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "a fourier perspective on model robustness in computer vision"
    - "learnability for the information bottleneck"
    - "the conditional entropy bottleneck"
    - "feature denoising for improving adversarial robustness"
    - "autoaugment learning augmentation policies from data"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"

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)