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: "motivating the rules of the game for adversarial example research"
  Abstract: Advances in machine learning have led to broad deployment of systems with impressive performance on important problems. Nonetheless, these systems can be induced to make errors on data that are surprisingly similar to examples the learned system handles correctly. The existence of these errors raises a variety of questions about out-of-sample generalization and whether bad actors might use such examples to abuse deployed systems. As a result of these security concerns, there has been a flurry of recent papers proposing algorithms to defend against such malicious perturbations of correctly handled examples. It is unclear how such misclassifications represent a different kind of security problem than other errors, or even other attacker-produced examples that have no specific relationship to an uncorrupted input. In this paper, we argue that adversarial example defense papers have, to date, mostly considered abstract, toy games that do not relate to any specific security concern. Furthermore, defense papers have not yet precisely described all the abilities and limitations of attackers that would be relevant in practical security. Towards this end, we establish a taxonomy of motivations, constraints, and abilities for more plausible adversaries. Finally, we provide a series of recommendations outlining a path forward for future work to more clearly articulate the threat model and perform more meaningful evaluation.

Connected through hyperedge HE-1 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence"):
  with members:
    - "towards ground truth evaluation of visual explanations"
    - "continual lifelong learning with neural networks a review"
    - "programmable neural network trojan for pre trained feature extractor"
    - "hidden trigger backdoor attacks"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "on evaluating adversarial robustness"
    - "explainable ai for trees from local explanations to global understanding"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-2 (induced by source paper "a survey of black box adversarial attacks on computer vision models"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "simple black box adversarial attacks"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "feature denoising for improving adversarial robustness"
    - "defense gan protecting classifiers against adversarial attacks using generative models"

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 "bayes probe distribution guided sampling for prediction level sets"):
  with members:
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "out of distribution detection in classifiers via generation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "pyro deep universal probabilistic programming"
    - "this looks like that deep learning for interpretable image recognition"
    - "sim to real learning agile locomotion for quadruped robots"
    - "certified adversarial robustness with additive noise"
    - "sanity checks for saliency maps"

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 machine learning industry perspectives"):
  with members:
    - "the deepfake detection challenge dfdc preview dataset"
    - "secml a python library for secure and explainable machine learning"
    - "failure modes in machine learning systems"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial music real world audio adversary against wake word detection system"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "adversarial reprogramming of neural networks"

Connected through hyperedge HE-7 (induced by source paper "fundamental tradeoffs between invariance and sensitivity to adversarial perturbations"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "procedural noise adversarial examples for black box attacks on deep convolutional networks"
    - "adversarial perceptual ad blocking meets adversarial machine learning"
    - "testing robustness against unforeseen adversaries"
    - "a fourier perspective on model robustness in computer vision"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "towards the first adversarially robust neural network model on mnist"
    - "defense gan protecting classifiers against adversarial attacks using generative models"

Connected through hyperedge HE-8 (induced by source paper "feature level malware obfuscation in deep learning"):
  with members:
    - "adversarial attacks and defenses in images graphs and text a review"
    - "effectiveness of adversarial examples and defenses for malware classification"
    - "a large scale investigation of obfuscation use in google play"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "exploring adversarial examples in malware detection"
    - "deep learning at the shallow end malware classification for non domain experts"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial perturbations against deep neural networks for malware classification"

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)