human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cross representation transferability of adversarial perturbations from spectrograms to audio waveforms"):
  Abstract: This paper shows the susceptibility of spectrogram-based audio classifiers to adversarial attacks and the transferability of such attacks to audio waveforms. Some commonly adversarial attacks to images have been applied to Mel-frequency and short-time Fourier transform spectrograms and such perturbed spectrograms are able to fool a 2D convolutional neural network (CNN) for music genre classification with a high fooling rate and high confidence. Such attacks produce perturbed spectrograms that are visually imperceptible by humans. Experimental results on a dataset of western music have shown that the 2D CNN achieves up to 81.87% of mean accuracy on legitimate examples and such a performance drops to 12.09% on adversarial examples. Furthermore, the audio signals reconstructed from the adversarial spectrograms produce audio waveforms that perceptually resemble the legitimate audio.

Includes members:
  Member "universal adversarial audio perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blind adversarial network perturbations")
  Member "unsupervised feature learning for environmental sound classification using cycle consistent generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "detection of adversarial attacks and characterization of adversarial subspace")
  Member "end to end environmental sound classification using a 1d convolutional neural network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal adversarial audio perturbations")
  Member "a robust approach for securing audio classification against adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "imperio robust over the air adversarial examples for automatic speech recognition systems")
  Member "sirenattack generating adversarial audio for end to end acoustic systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "structure invariant testing for machine translation")
  Member "randomly weighted cnns for music audio classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "self supervised learning by cross modal audio video clustering")
  Member "multi label music genre classification from audio text and images using deep features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "machine learning for music genre multifaceted review and experimentation with audioset")
  Member "multi level and multi scale feature aggregation using pretrained convolutional neural networks for music auto tagging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multinet multi stream feature aggregation and geometric loss strategy for multi task learning")

Question: Please tell me which class the source paper 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)