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 perturbations for speech recognition systems"
  Abstract: In this work, we demonstrate the existence of universal adversarial audio perturbations that cause mis-transcription of audio signals by automatic speech recognition (ASR) systems. We propose an algorithm to find a single quasi-imperceptible perturbation, which when added to any arbitrary speech signal, will most likely fool the victim speech recognition model. Our experiments demonstrate the application of our proposed technique by crafting audio-agnostic universal perturbations for the state-of-the-art ASR system -- Mozilla DeepSpeech. Additionally, we show that such perturbations generalize to a significant extent across models that are not available during training, by performing a transferability test on a WaveNet based ASR system.

Connected through hyperedge HE-1 (induced by source paper "adversarial deepfakes evaluating vulnerability of deepfake detectors to adversarial examples"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "deferred neural rendering image synthesis using neural textures"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "the deepfake detection challenge dfdc preview dataset"
    - "hybrid lstm and encoder decoder architecture for detection of image forgeries"
    - "wav2pix speech conditioned face generation using generative adversarial networks"
    - "mesonet a compact facial video forgery detection network"

Connected through hyperedge HE-2 (induced by source paper "universal adversarial audio perturbations"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "adversarial initialization when your network performs the way i want"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "unsupervised text style transfer using language models as discriminators"
    - "knockoff nets stealing functionality of black box models"

Connected through hyperedge HE-3 (induced by source paper "active subspace of neural networks structural analysis and universal attacks"):
  with members:
    - "universal adversarial audio perturbations"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "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-4 (induced by source paper "imperio robust over the air adversarial examples for automatic speech recognition systems"):
  with members:
    - "universal adversarial audio perturbations"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "universal adversarial perturbations for cnn classifiers in eeg based bcis"):
  with members:
    - "on the vulnerability of cnn classifiers in eeg based bcis"
    - "active learning for black box adversarial attacks in eeg based brain computer interfaces"
    - "adversarial patch"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "audio adversarial examples targeted attacks on speech to text"
    - "deep learning with convolutional neural networks for eeg decoding and visualization"
    - "feature selection a data perspective"

Connected through hyperedge HE-6 (induced by source paper "universal adversarial examples in speech command classification"):
  with members:
    - "universal adversarial audio perturbations"
    - "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-7 (induced by source paper "real time adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "robust audio adversarial example for a physical attack"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "audio adversarial examples targeted attacks on speech to text"
    - "adversarial perturbations against real time video classification systems"
    - "an overview of vulnerabilities of voice controlled systems"

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)