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 prediction and composition by means of variational autoencoders"):
  Abstract: This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way in order to address two different problems: music representation into the latent space, and using this representation to make predictions of the future values of the musical piece. This approach was trained with different songs of a classical composer. As a result, the system can represent the music in the latent space, and make accurate predictions. Therefore, the system can be used to compose new music either from an existing piece or from a random starting point. An additional feature of this system is that a small dataset was used for training. However, results show that the system is able to return accurate representations and predictions in unseen data.

Includes members:
  Member "non monotonic sequential text generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blank language models")
  Member "convolutional generative adversarial networks with binary neurons for polyphonic music generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dawson a domain adaptive few shot generation framework")
  Member "generative timbre spaces regularizing variational auto encoders with perceptual metrics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural drum machine an interactive system for real time synthesis of drum sounds")
  Member "a hierarchical latent vector model for learning long term structure in music":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "music generation by deep learning challenges and directions")
  Member "midinet a convolutional generative adversarial network for symbolic domain music generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "music generation by deep learning challenges and directions")
  Member "neural audio synthesis of musical notes with wavenet autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ddsp differentiable digital signal processing")
  Member "learning latent representations for speech generation and transformation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep autotuner a pitch correcting network for singing performances")
  Member "seqgan sequence generative adversarial nets with policy gradient":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")

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)