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: "universal adversarial audio perturbations"
  Abstract: We demonstrate the existence of universal adversarial perturbations, which can fool a family of audio classification architectures, for both targeted and untargeted attack scenarios. We propose two methods for finding such perturbations. The first method is based on an iterative, greedy approach that is well-known in computer vision: it aggregates small perturbations to the input so as to push it to the decision boundary. The second method, which is the main contribution of this work, is a novel penalty formulation, which finds targeted and untargeted universal adversarial perturbations. Differently from the greedy approach, the penalty method minimizes an appropriate objective function on a batch of samples. Therefore, it produces more successful attacks when the number of training samples is limited. Moreover, we provide a proof that the proposed penalty method theoretically converges to a solution that corresponds to universal adversarial perturbations. We also demonstrate that it is possible to provide successful attacks using the penalty method when only one sample from the target dataset is available for the attacker. Experimental results on attacking five 1D CNN architectures have shown attack success rates higher than 85.4% and 83.1% for targeted and untargeted attacks, respectively using the proposed penalty method.

Connected through hyperedge HE-1 (induced by source paper "blind adversarial network perturbations"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "mockingbird defending against deep learning based website fingerprinting attacks with adversarial traces"
    - "deep fingerprinting undermining website fingerprinting defenses with deep learning"
    - "deepcorr strong flow correlation attacks on tor using deep learning"
    - "automated website fingerprinting through deep learning"
    - "var cnn and dynaflow improved attacks and defenses for website fingerprinting"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "learning universal adversarial perturbations with generative models"

Connected through hyperedge HE-2 (induced by source paper "active subspace of neural networks structural analysis and universal attacks"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "universal adversarial perturbations for speech recognition systems"
    - "progressive dnn compression a key to achieve ultra high weight pruning and quantization rates using admm"
    - "bayesian tensorized neural networks with automatic rank selection"
    - "mildly overparametrized neural nets can memorize training data efficiently"
    - "adversarial examples a complete characterisation of the phenomenon"
    - "gradient descent based d optimal design for the least squares polynomial approximation"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-3 (induced by source paper "imperio robust over the air adversarial examples for automatic speech recognition systems"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "universal adversarial perturbations for speech recognition systems"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "practical hidden voice attacks against speech and speaker recognition systems"
    - "on evaluating adversarial robustness"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"
    - "targeted adversarial examples for black box audio systems"

Connected through hyperedge HE-4 (induced by source paper "robust over the air adversarial examples against automatic speech recognition systems"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "rwth asr systems for librispeech hybrid vs attention w o data augmentation"
    - "practical hidden voice attacks against speech and speaker recognition systems"
    - "on evaluating adversarial robustness"
    - "noise flooding for detecting audio adversarial examples against automatic speech recognition"
    - "targeted adversarial examples for black box audio systems"
    - "commandersong a systematic approach for practical adversarial voice recognition"

Connected through hyperedge HE-5 (induced by source paper "universal adversarial examples in speech command classification"):
  with members:
    - "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"
    - "continuous authentication for voice assistants"

Connected through hyperedge HE-6 (induced by source paper "cross representation transferability of adversarial perturbations from spectrograms to audio waveforms"):
  with members:
    - "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "a robust approach for securing audio classification against adversarial attacks"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "randomly weighted cnns for music audio classification"
    - "multi label music genre classification from audio text and images using deep features"
    - "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging"
    - "cnn architectures for large scale audio classification"

Connected through hyperedge HE-7 (induced by source paper "simple iterative method for generating targeted universal adversarial perturbations"):
  with members:
    - "adversarial patch"
    - "adversarial robustness toolbox v1 0 0"
    - "learning universal adversarial perturbations with generative models"
    - "universal adversarial perturbations"
    - "deepfool a simple and accurate method to fool deep 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.LG(Machine Learning)