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: "benchmarking neural network robustness to common corruptions and surface variations"
  Abstract: In this paper we establish rigorous benchmarks for image classifier robustness. Our first benchmark, ImageNet-C, standardizes and expands the corruption robustness topic, while showing which classifiers are preferable in safety-critical applications. Unlike recent robustness research, this benchmark evaluates performance on commonplace corruptions not worst-case adversarial corruptions. We find that there are negligible changes in relative corruption robustness from AlexNet to ResNet classifiers, and we discover ways to enhance corruption robustness. Then we propose a new dataset called Icons-50 which opens research on a new kind of robustness, surface variation robustness. With this dataset we evaluate the frailty of classifiers on new styles of known objects and unexpected instances of known classes. We also demonstrate two methods that improve surface variation robustness. Together our benchmarks may aid future work toward networks that learn fundamental class structure and also robustly generalize.

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"
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "compressed sensing with invertible generative models and dependent noise"):
  with members:
    - "deep compressed sensing"
    - "invertible generative models for inverse problems mitigating representation error and dataset bias"
    - "invertible residual networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "density estimation using real nvp"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-3 (induced by source paper "exploring the origins and prevalence of texture bias in convolutional neural networks"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "online normalization for training neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "psyphy a psychophysics driven evaluation framework for visual recognition"
    - "when does label smoothing help"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "stand alone self attention in vision models"
    - "are perceptually aligned gradients a general property of robust classifiers"

Connected through hyperedge HE-4 (induced by source paper "on evaluating adversarial robustness"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "the efficacy of shield under different threat models"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "generative adversarial examples"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "logit pairing methods can fool gradient based attacks"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-5 (induced by source paper "improving robustness without sacrificing accuracy with patch gaussian augmentation"):
  with members:
    - "using videos to evaluate image model robustness"
    - "adversarial examples are a natural consequence of test error in noise"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "learning robust representations by projecting superficial statistics out"
    - "do imagenet classifiers generalize to imagenet"
    - "using pre training can improve model robustness and uncertainty"
    - "dropblock a regularization method for convolutional networks"
    - "a kernel theory of modern data augmentation"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples improve image recognition"):
  with members:
    - "improving robustness without sacrificing accuracy with patch gaussian augmentation"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "diminishing the effect of adversarial perturbations via refining feature representation"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "enhancing the robustness of deep neural networks by boundary conditional gan"
    - "on evaluating adversarial robustness"
    - "medical image synthesis for data augmentation and anonymization using generative adversarial networks"
    - "vulnerability analysis of chest x ray image classification against adversarial attacks"
    - "feature denoising for improving adversarial robustness"
    - "blind pre processing a robust defense method against adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "on the robustness of deep k nearest neighbors"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "a survey on deep learning in medical image analysis"
    - "interpretable convolutional neural 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)