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: "procedural noise adversarial examples for black box attacks on deep convolutional networks"
  Abstract: Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples---perturbed inputs specifically designed to produce intentional errors in the learning algorithms at test time. Existing input-agnostic adversarial perturbations exhibit interesting visual patterns that are currently unexplained. In this paper, we introduce a structured approach for generating Universal Adversarial Perturbations (UAPs) with procedural noise functions. Our approach unveils the systemic vulnerability of popular DCN models like Inception v3 and YOLO v3, with single noise patterns able to fool a model on up to 90% of the dataset. Procedural noise allows us to generate a distribution of UAPs with high universal evasion rates using only a few parameters. Additionally, we propose Bayesian optimization to efficiently learn procedural noise parameters to construct inexpensive untargeted black-box attacks. We demonstrate that it can achieve an average of less than 10 queries per successful attack, a 100-fold improvement on existing methods. We further motivate the use of input-agnostic defences to increase the stability of models to adversarial perturbations. The universality of our attacks suggests that DCN models may be sensitive to aggregations of low-level class-agnostic features. These findings give insight on the nature of some universal adversarial perturbations and how they could be generated in other applications.

Connected through hyperedge HE-1 (induced by source paper "fundamental tradeoffs between invariance and sensitivity to adversarial perturbations"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "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"
    - "excessive invariance causes adversarial vulnerability"

Connected through hyperedge HE-2 (induced by source paper "taking care of the discretization problem a black box adversarial image attack in discrete integer domain"):
  with members:
    - "towards improved testing for deep learning"
    - "one pixel attack for fooling deep neural networks"
    - "on the design of black box adversarial examples by leveraging gradient free optimization and operator splitting method"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "spatially transformed adversarial examples"
    - "formal security analysis of neural networks using symbolic intervals"
    - "commandersong a systematic approach for practical adversarial voice recognition"
    - "a dual approach to scalable verification of deep networks"

Connected through hyperedge HE-3 (induced by source paper "universal adversarial perturbations to understand robustness of texture vs shape biased training"):
  with members:
    - "improving the robustness of imagenet classifiers using elements of human visual cognition"
    - "adversarial training and robustness for multiple perturbations"
    - "transfer of adversarial robustness between perturbation types"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "sensitivity of deep convolutional networks to gabor noise"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "universal adversarial training"
    - "generalizable data free objective for crafting universal adversarial perturbations"

Connected through hyperedge HE-4 (induced by source paper "black box adversarial attacks with bayesian optimization"):
  with members:
    - "hopskipjumpattack a query efficient decision based attack"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"
    - "query limited black box attacks to classifiers"
    - "practical black box attacks against machine learning"
    - "delving into transferable adversarial examples and black box attacks"
    - "towards evaluating the robustness of neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "sensitivity of deep convolutional networks to gabor noise"):
  with members:
    - "nag network for adversary generation"
    - "universal adversarial perturbations"
    - "deep residual learning for image recognition"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "rethinking the inception architecture for computer vision"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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.CR(Cryptography and Security)