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: "ask acquire and attack data free uap generation using class impressions"
  Abstract: Deep learning models are susceptible to input specific noise, called adversarial perturbations. Moreover, there exist input-agnostic noise, called Universal Adversarial Perturbations (UAP) that can affect inference of the models over most input samples. Given a model, there exist broadly two approaches to craft UAPs: (i) data-driven: that require data, and (ii) data-free: that do not require data samples. Data-driven approaches require actual samples from the underlying data distribution and craft UAPs with high success (fooling) rate. However, data-free approaches craft UAPs without utilizing any data samples and therefore result in lesser success rates. In this paper, for data-free scenarios, we propose a novel approach that emulates the effect of data samples with class impressions in order to craft UAPs using data-driven objectives. Class impression for a given pair of category and model is a generic representation (in the input space) of the samples belonging to that category. Further, we present a neural network based generative model that utilizes the acquired class impressions to learn crafting UAPs. Experimental evaluation demonstrates that the learned generative model, (i) readily crafts UAPs via simple feed-forwarding through neural network layers, and (ii) achieves state-of-the-art success rates for data-free scenario and closer to that for data-driven setting without actually utilizing any data samples.

Connected through hyperedge HE-1 (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"
    - "universal adversarial training"
    - "divide denoise and defend against adversarial attacks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "fast feature fool a data independent approach to universal adversarial perturbations"

Connected through hyperedge HE-3 (induced by source paper "degan data enriching gan for retrieving representative samples from a trained classifier"):
  with members:
    - "learning from higher layer feature visualizations"
    - "zero shot knowledge distillation in deep networks"
    - "few sample knowledge distillation for efficient network compression"
    - "knowledge distillation from few samples"
    - "end to end incremental learning"
    - "domain adaptation for visual applications a comprehensive survey"
    - "statistics of deep generated images"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-4 (induced by source paper "zero shot knowledge distillation in deep networks"):
  with members:
    - "a kernel theory of modern data augmentation"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "data free knowledge distillation for deep neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "striving for simplicity the all convolutional net"
    - "imagenet large scale visual recognition challenge"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-5 (induced by source paper "generalizable data free objective for crafting universal adversarial perturbations"):
  with members:
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "switching convolutional neural network for crowd counting"
    - "geometric loss functions for camera pose regression with deep learning"
    - "nag network for adversary generation"
    - "scaling the scattering transform deep hybrid networks"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"

Connected through hyperedge HE-6 (induced by source paper "universal adversarial training"):
  with members:
    - "adversarial attacks on copyright detection systems"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "playing the game of universal adversarial perturbations"
    - "adversarial patch"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "defending against universal perturbations with shared adversarial training"
    - "generating adversarial examples with adversarial networks"
    - "generalizable data free objective for crafting 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)