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: "a robust approach for securing audio classification against adversarial attacks"
  Abstract: Adversarial audio attacks can be considered as a small perturbation unperceptive to human ears that is intentionally added to the audio signal and causes a machine learning model to make mistakes. This poses a security concern about the safety of machine learning models since the adversarial attacks can fool such models toward the wrong predictions. In this paper we first review some strong adversarial attacks that may affect both audio signals and their 2D representations and evaluate the resiliency of the most common machine learning model, namely deep learning models and support vector machines (SVM) trained on 2D audio representations such as short time Fourier transform (STFT), discrete wavelet transform (DWT) and cross recurrent plot (CRP) against several state-of-the-art adversarial attacks. Next, we propose a novel approach based on pre-processed DWT representation of audio signals and SVM to secure audio systems against adversarial attacks. The proposed architecture has several preprocessing modules for generating and enhancing spectrograms including dimension reduction and smoothing. We extract features from small patches of the spectrograms using speeded up robust feature (SURF) algorithm which are further used to generate a codebook using the K-Means++ algorithm. Finally, codewords are used to train a SVM on the codebook of the SURF-generated vectors. All these steps yield to a novel approach for audio classification that provides a good trade-off between accuracy and resilience. Experimental results on three environmental sound datasets show the competitive performance of proposed approach compared to the deep neural networks both in terms of accuracy and robustness against strong adversarial attacks.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"):
  with members:
    - "end to end environmental sound classification using a 1d convolutional neural network"
    - "a novel orthogonal direction mesh adaptive direct search approach for svm hyperparameter tuning"
    - "randomly weighted cnns for music audio classification"
    - "environmental sound classification based on multi temporal resolution convolutional neural network combining with multi level features"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "robust over the air adversarial examples against automatic speech recognition systems"):
  with members:
    - "universal adversarial audio perturbations"
    - "robust audio adversarial example for a physical attack"
    - "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-4 (induced by source paper "a fast online cascaded regression algorithm for face alignment"):
  with members:
    - "3 d personvlad learning deep global representations for video based person reidentification"
    - "cycle consistent deep generative hashing for cross modal retrieval"
    - "cross entropy adversarial view adaptation for person re identification"
    - "face alignment in full pose range a 3d total solution"
    - "robust tracking via weighted online extreme learning machine"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "a functional regression approach to facial landmark tracking"
    - "effective multi query expansions collaborative deep networks for robust landmark retrieval"

Connected through hyperedge HE-5 (induced by source paper "cross representation transferability of adversarial perturbations from spectrograms to audio waveforms"):
  with members:
    - "universal adversarial audio perturbations"
    - "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"
    - "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-6 (induced by source paper "detection of adversarial attacks and characterization of adversarial subspace"):
  with members:
    - "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "deepfense online accelerated defense against adversarial deep learning"
    - "on the statistical detection of adversarial examples"
    - "adversarial diversity and hard positive generation"
    - "delving into transferable adversarial examples and black box attacks"

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)