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: "upset and angri breaking high performance image classifiers"
  Abstract: In this paper, targeted fooling of high performance image classifiers is achieved by developing two novel attack methods. The first method generates universal perturbations for target classes and the second generates image specific perturbations. Extensive experiments are conducted on MNIST and CIFAR10 datasets to provide insights about the proposed algorithms and show their effectiveness.

Connected through hyperedge HE-1 (induced by source paper "neural network model extraction attacks in edge devices by hearing architectural hints"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "tvm end to end optimization stack for deep learning"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "sok security and privacy in machine learning"
    - "stealing hyperparameters in machine learning"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-2 (induced by source paper "jumprelu a retrofit defense strategy for adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "trust region based adversarial attack on neural networks"
    - "deflecting adversarial attacks with pixel deflection"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "moving target defense for deep visual sensing against adversarial examples"):
  with members:
    - "simple physical adversarial examples against end to end autonomous driving models"
    - "on evaluating adversarial robustness"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-4 (induced by source paper "poba ga perturbation optimized black box adversarial attacks via genetic algorithm"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "identify susceptible locations in medical records via adversarial attacks on deep predictive models"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "security analysis and enhancement of model compressed deep learning systems under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "asp a fast adversarial attack example generation framework based on adversarial saliency prediction"

Connected through hyperedge HE-5 (induced by source paper "heuristic black box adversarial attacks on video recognition models"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "real world anomaly detection in surveillance videos"
    - "sparse adversarial perturbations for videos"
    - "black box adversarial attacks with limited queries and information"
    - "query efficient hard label black box attack an optimization based approach"
    - "adversarial perturbations against real time video classification systems"

Connected through hyperedge HE-6 (induced by source paper "on the robustness of human pose estimation"):
  with members:
    - "characterizing adversarial examples based on spatial consistency information for semantic segmentation"
    - "generating adversarial examples with adversarial networks"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "generative adversarial perturbations"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "adversarial examples for semantic segmentation and object detection"
    - "multi context attention for human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "query free attacks on industry grade face recognition systems under resource constraints"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial attacks on neural network policies"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "mixup beyond empirical risk minimization"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"
    - "on the effectiveness of defensive distillation"

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)