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: "constructing unrestricted adversarial examples with generative models"
  Abstract: Adversarial examples are typically constructed by perturbing an existing data point within a small matrix norm, and current defense methods are focused on guarding against this type of attack. In this paper, we propose unrestricted adversarial examples, a new threat model where the attackers are not restricted to small norm-bounded perturbations. Different from perturbation-based attacks, we propose to synthesize unrestricted adversarial examples entirely from scratch using conditional generative models. Specifically, we first train an Auxiliary Classifier Generative Adversarial Network (AC-GAN) to model the class-conditional distribution over data samples. Then, conditioned on a desired class, we search over the AC-GAN latent space to find images that are likely under the generative model and are misclassified by a target classifier. We demonstrate through human evaluation that unrestricted adversarial examples generated this way are legitimate and belong to the desired class. Our empirical results on the MNIST, SVHN, and CelebA datasets show that unrestricted adversarial examples can bypass strong adversarial training and certified defense methods designed for traditional adversarial attacks.

Connected through hyperedge HE-1 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "image super resolution as a defense against adversarial attacks"
    - "explaining convolutional neural networks using softmax gradient layer wise relevance propagation"
    - "yet another but more efficient black box adversarial attack tiling and evolution strategies"
    - "natural adversarial examples"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "interpreting adversarially trained convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "vec2face unveil human faces from their blackbox features in face recognition"):
  with members:
    - "black box adversarial attacks with bayesian optimization"
    - "shrinkteanet million scale lightweight face recognition via shrinking teacher student networks"
    - "adacos adaptively scaling cosine logits for effectively learning deep face representations"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning towards minimum hyperspherical energy"
    - "black box adversarial attacks with limited queries and information"
    - "mobiface a lightweight deep learning face recognition on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "metrics and methods for robustness evaluation of neural networks with generative models"):
  with members:
    - "using videos to evaluate image model robustness"
    - "seeing what a gan cannot generate"
    - "adversarial examples are a natural consequence of test error in noise"
    - "natural adversarial examples"
    - "image synthesis with a single robust classifier"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"

Connected through hyperedge HE-4 (induced by source paper "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "functional adversarial attacks"
    - "wasserstein adversarial examples via projected sinkhorn iterations"
    - "theoretically principled trade off between robustness and accuracy"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "adversarial training for free"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "generating realistic unrestricted adversarial inputs using dual objective gan training"

Connected through hyperedge HE-5 (induced by source paper "semantic adversarial perturbations using learnt representations"):
  with members:
    - "semanticadv generating adversarial examples via attribute conditional image editing"
    - "algorithms for verifying deep neural networks"
    - "generating semantic adversarial examples with differentiable rendering"
    - "testing robustness against unforeseen adversaries"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "adaptive generation of unrestricted adversarial inputs"
    - "semantic adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "adversarial camouflage hiding physical world attacks with natural styles"):
  with members:
    - "skip connections matter on the transferability of adversarial examples generated with resnets"
    - "understanding adversarial attacks on deep learning based medical image analysis systems"
    - "black box adversarial attacks on video recognition models"
    - "adversarial t shirt evading person detectors in a physical world"
    - "semantic adversarial examples"
    - "generating adversarial examples with adversarial networks"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-7 (induced by source paper "at gan an adversarial generator model for non constrained adversarial examples"):
  with members:
    - "a simple explanation for the existence of adversarial examples with small hamming distance"
    - "improving the generalization of adversarial training with domain adaptation"
    - "exploring the space of black box attacks on deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "defense against adversarial attacks using high level representation guided denoiser"
    - "ape gan adversarial perturbation elimination with gan"
    - "improved training of wasserstein gans"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-8 (induced by source paper "ai gan attack inspired generation of adversarial examples"):
  with members:
    - "advgan harnessing latent layers for adversary generation"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating adversarial examples with adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "generative adversarial perturbations"
    - "generative adversarial text to image synthesis"
    - "towards evaluating the robustness of 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)