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: "with friends like these who needs adversaries"
  Abstract: The vulnerability of deep image classification networks to adversarial attack is now well known, but less well understood. Via a novel experimental analysis, we illustrate some facts about deep convolutional networks for image classification that shed new light on their behaviour and how it connects to the problem of adversaries. In short, the celebrated performance of these networks and their vulnerability to adversarial attack are simply two sides of the same coin: the input image-space directions along which the networks are most vulnerable to attack are the same directions which they use to achieve their classification performance in the first place. We develop this result in two main steps. The first uncovers the fact that classes tend to be associated with specific image-space directions. This is shown by an examination of the class-score outputs of nets as functions of 1D movements along these directions. This provides a novel perspective on the existence of universal adversarial perturbations. The second is a clear demonstration of the tight coupling between classification performance and vulnerability to adversarial attack within the spaces spanned by these directions. Thus, our analysis resolves the apparent contradiction between accuracy and vulnerability. It provides a new perspective on much of the prior art and reveals profound implications for efforts to construct neural nets that are both accurate and robust to adversarial attack.

Connected through hyperedge HE-1 (induced by source paper "parot a practical framework for robust deep neural network training"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "a provable defense for deep residual networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "symbolic execution for deep neural networks"
    - "mixtrain scalable training of formally robust neural networks"
    - "using machine learning safely in automotive software an assessment and adaption of software process requirements in iso 26262"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "auditing and debugging deep learning models via decision boundaries individual level and group level analysis"):
  with members:
    - "an evaluation of the human interpretability of explanation"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "measurable counterfactual local explanations for any classifier"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "on the accuracy of influence functions for measuring group effects"
    - "how do i fool you manipulating user trust via misleading black box explanations"
    - "refining the structure of neural networks using matrix conditioning"
    - "interpreting neural networks using flip points"

Connected through hyperedge HE-3 (induced by source paper "hold me tight influence of discriminative features on deep network boundaries"):
  with members:
    - "on the effectiveness of low frequency perturbations"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "a fourier perspective on model robustness in computer vision"
    - "pytorch an imperative style high performance deep learning library"
    - "robustness via curvature regularization and vice versa"
    - "adversarial spheres"
    - "on the structural sensitivity of deep convolutional networks to the directions of fourier basis functions"
    - "on the decision boundary of deep neural networks"

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"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "logit pairing methods can fool gradient based attacks"

Connected through hyperedge HE-5 (induced by source paper "detecting and diagnosing adversarial images with class conditional capsule reconstructions"):
  with members:
    - "mnist c a robustness benchmark for computer vision"
    - "deepcaps going deeper with capsule networks"
    - "are odds really odd bypassing statistical detection of adversarial examples"
    - "on the vulnerability of capsule networks to adversarial attacks"
    - "semantic image synthesis with spatially adaptive normalization"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "adversarial attacks and defences competition"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-6 (induced by source paper "smoothfool an efficient framework for computing smooth adversarial perturbations"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "spatially transformed adversarial examples"
    - "semantic adversarial examples"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "fast geometrically perturbed adversarial faces"
    - "divide denoise and defend against adversarial attacks"
    - "are adversarial examples inevitable"

Connected through hyperedge HE-7 (induced by source paper "investigating decision boundaries of trained neural networks"):
  with members:
    - "interpreting neural networks using flip points"
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "exploring generalization in deep learning"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "formal guarantees on the robustness of a classifier against adversarial manipulation"
    - "a boundary tilting persepective on the phenomenon of adversarial examples"
    - "why should i trust you explaining the predictions of any classifier"
    - "deepfool a simple and accurate method to fool deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "defending against universal perturbations with shared adversarial training"):
  with members:
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "playing the game of universal adversarial perturbations"
    - "universal adversarial training"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "nag network for adversary generation"

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)