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: "end to end environmental sound classification using a 1d convolutional neural network"
  Abstract: In this paper, we present an end-to-end approach for environmental sound classification based on a 1D Convolution Neural Network (CNN) that learns a representation directly from the audio signal. Several convolutional layers are used to capture the signal's fine time structure and learn diverse filters that are relevant to the classification task. The proposed approach can deal with audio signals of any length as it splits the signal into overlapped frames using a sliding window. Different architectures considering several input sizes are evaluated, including the initialization of the first convolutional layer with a Gammatone filterbank that models the human auditory filter response in the cochlea. The performance of the proposed end-to-end approach in classifying environmental sounds was assessed on the UrbanSound8k dataset and the experimental results have shown that it achieves 89% of mean accuracy. Therefore, the propose approach outperforms most of the state-of-the-art approaches that use handcrafted features or 2D representations as input. Furthermore, the proposed approach has a small number of parameters compared to other architectures found in the literature, which reduces the amount of data required for training.

Connected through hyperedge HE-1 (induced by source paper "universal adversarial audio perturbations"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "universal adversarial perturbations for speech recognition systems"
    - "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-2 (induced by source paper "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network"):
  with members:
    - "a robust approach for securing audio classification against adversarial attacks"
    - "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 "environment sound classification using multiple feature channels and attention based deep convolutional neural network"):
  with members:
    - "multi stream network with temporal attention for environmental sound classification"
    - "randomly weighted cnns for music audio classification"
    - "study of residual networks for image recognition"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "deep convolutional neural network with mixup for environmental sound classification"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"
    - "learning from between class examples for deep sound recognition"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"

Connected through hyperedge HE-4 (induced by source paper "urban sound tagging using convolutional neural networks"):
  with members:
    - "learning sound event classifiers from web audio with noisy labels"
    - "attention based convolutional recurrent neural network for environmental sound classification"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "random erasing data augmentation"
    - "audio spectrogram representations for processing with convolutional neural networks"
    - "mixup beyond empirical risk minimization"

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"
    - "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"

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.SD(Sound)