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: "analysis of universal adversarial perturbations"
  Abstract: Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers. In this paper, we propose the first quantitative analysis of the robustness of classifiers to universal perturbations, and draw a formal link between the robustness to universal perturbations, and the geometry of the decision boundary. Specifically, we establish theoretical bounds on the robustness of classifiers under two decision boundary models (flat and curved models). We show in particular that the robustness of deep networks to universal perturbations is driven by a key property of their curvature: there exists shared directions along which the decision boundary of deep networks is systematically positively curved. Under such conditions, we prove the existence of small universal perturbations. Our analysis further provides a novel geometric method for computing universal perturbations, in addition to explaining their properties.

Connected through hyperedge HE-1 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning"):
  with members:
    - "adversarial policies attacking deep reinforcement learning"
    - "distilling policy distillation"
    - "sequential triggers for watermarking of deep reinforcement learning policies"
    - "deceptive reinforcement learning under adversarial manipulations on cost signals"
    - "adversarial exploitation of policy imitation"
    - "wasserstein robust reinforcement learning"
    - "rl based method for benchmarking the adversarial resilience and robustness of deep reinforcement learning policies"
    - "analysis and improvement of adversarial training in dqn agents with adversarially guided exploration age"

Connected through hyperedge HE-2 (induced by source paper "practical fast gradient sign attack against mammographic image classifier"):
  with members:
    - "detecting sybil attacks using proofs of work and location in vanets"
    - "privacy preserving smart parking system using blockchain and private information retrieval"
    - "b ride ride sharing with privacy preservation trust and fair payment atop public blockchain"
    - "privacy preserving and collusion resistant charging coordination schemes for smart grid"
    - "on sharing models instead of data using mimic learning for smart health applications"
    - "expansion of cyber attack data from unbalanced datasets using generative techniques"
    - "blockchain based charging coordination mechanism for smart grid energy storage units"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-3 (induced by source paper "defending against universal attacks through selective feature regeneration"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "adversarial camera stickers a physical camera based attack on deep learning systems"
    - "deepcorrect correcting dnn models against image distortions"
    - "playing the game of universal adversarial perturbations"
    - "feature denoising for improving adversarial robustness"
    - "ask acquire and attack data free uap generation using class impressions"
    - "universal adversarial training"

Connected through hyperedge HE-4 (induced by source paper "one pixel attack for fooling deep neural networks"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "self organizing classifiers first steps in structured evolutionary machine learning"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "on the suitability of l_p norms for creating and preventing adversarial examples"
    - "self organizing classifiers and niched fitness"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "classification regions of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "indirect local attacks for context aware semantic segmentation networks"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "attacking optical flow"
    - "advertorch v0 1 an adversarial robustness toolbox based on pytorch"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "dual attention network for scene segmentation"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "context encoding for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "detecting adversarial samples using influence functions and nearest neighbors"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "defending against adversarial examples with k nearest neighbor"
    - "on evaluating adversarial robustness"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "dnn or k nn that is the generalize vs memorize question"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "a method for computing class wise universal adversarial perturbations"):
  with members:
    - "scaling provable adversarial defenses"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "exploring the space of black box attacks on deep neural networks"
    - "black box adversarial attacks with limited queries and information"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "nag network for adversary generation"

Connected through hyperedge HE-8 (induced by source paper "adaptive gradient for adversarial perturbations generation"):
  with members:
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "adversarial examples for semantic segmentation and object detection"
    - "on the statistical detection of adversarial examples"
    - "identity mappings in deep residual networks"
    - "universal adversarial perturbations"

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)