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 "classical music generation in distinct dastgahs with alimnet acgan"):
  Abstract: In this paper AlimNet (With respect to great musician, Alim Qasimov) an auxiliary generative adversarial deep neural network (ACGAN) for generating music categorically, is used. This proposed network is a conditional ACGAN to condition the generation process on music tracks which has a hybrid architecture, composing of different kind of layers of neural networks. The employed music dataset is MICM which contains 1137 music samples (506 violins and 631 straw) with seven types of classical music Dastgah labels. To extract both temporal and spectral features, Short-Time Fourier Transform (STFT) is applied to convert input audio signals from time domain to time-frequency domain. GANs are composed of a generator for generating new samples and a discriminator to help generator making better samples. Samples in time-frequency domain are used to train discriminator in fourteen classes (seven Dastgahs and two instruments). The outputs of the conditional ACGAN are also artificial music samples in those mentioned scales in time-frequency domain. Then the output of the generator is transformed by Inverse STFT (ISTFT). Finally, randomly ten generated music samples (five violin and five straw samples) are given to ten musicians to rate how exact the samples are and the overall result was 76.5%.

Includes members:
  Member "instrument independent dastgah recognition of iranian classical music using azarnet":
    also connected through other hyperedges:
      (none)
  Member "midinet a convolutional generative adversarial network for symbolic domain music generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "music generation by deep learning challenges and directions")
  Member "a report on sound event detection with different binaural features":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sound event localization and detection of overlapping sources using convolutional recurrent neural networks")
  Member "convolutional recurrent neural networks for polyphonic sound event detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sound event detection with depthwise separable and dilated convolutions")
  Member "neural audio synthesis of musical notes with wavenet autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ddsp differentiable digital signal processing")
  Member "svsgan singing voice separation via generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial generation of time frequency features with application in audio synthesis")
  Member "tac gan text conditioned auxiliary classifier generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "neural discrete representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations by predicting bags of visual words")

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)