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: "fast feature fool a data independent approach to universal adversarial perturbations"
  Abstract: State-of-the-art object recognition Convolutional Neural Networks (CNNs) are shown to be fooled by image agnostic perturbations, called universal adversarial perturbations. It is also observed that these perturbations generalize across multiple networks trained on the same target data. However, these algorithms require training data on which the CNNs were trained and compute adversarial perturbations via complex optimization. The fooling performance of these approaches is directly proportional to the amount of available training data. This makes them unsuitable for practical attacks since its unreasonable for an attacker to have access to the training data. In this paper, for the first time, we propose a novel data independent approach to generate image agnostic perturbations for a range of CNNs trained for object recognition. We further show that these perturbations are transferable across multiple network architectures trained either on same or different data. In the absence of data, our method generates universal adversarial perturbations efficiently via fooling the features learned at multiple layers thereby causing CNNs to misclassify. Experiments demonstrate impressive fooling rates and surprising transferability for the proposed universal perturbations generated without any training data.

Connected through hyperedge HE-1 (induced by source paper "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"):
  with members:
    - "adversarial attacks and defences competition"
    - "group normalization"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "universal adversarial training"

Connected through hyperedge HE-2 (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-3 (induced by source paper "cross domain transferability of adversarial perturbations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"
    - "evading defenses to transferable adversarial examples by translation invariant attacks"
    - "the relativistic discriminator a key element missing from standard gan"
    - "ask acquire and attack data free uap generation using class impressions"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "generating adversarial examples with adversarial networks"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "convergence and margin of adversarial training on separable data"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "does data augmentation lead to positive margin"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "universal adversarial training"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-6 (induced by source paper "universal adversarial perturbation for text classification"):
  with members:
    - "identify susceptible locations in medical records via adversarial attacks on deep predictive models"
    - "textbugger generating adversarial text against real world applications"
    - "deep text classification can be fooled"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "universal adversarial training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hotflip white box adversarial examples for text classification"
    - "analysis of universal adversarial perturbations"

Connected through hyperedge HE-7 (induced by source paper "universal adversarial examples in speech command classification"):
  with members:
    - "universal adversarial audio perturbations"
    - "universal adversarial perturbations for speech recognition systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "an overview of vulnerabilities of voice controlled systems"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

Connected through hyperedge HE-8 (induced by source paper "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"):
  with members:
    - "universal perturbation attack against image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "mitigating adversarial effects through randomization"
    - "practical black box attacks against machine learning"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "universal adversarial perturbations"
    - "delving into transferable adversarial examples and black box attacks"
    - "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.CV(Computer Vision and Pattern Recognition)