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 "this time with feeling learning expressive musical performance"):
  Abstract: Music generation has generally been focused on either creating scores or interpreting them. We discuss differences between these two problems and propose that, in fact, it may be valuable to work in the space of direct $\it performance$ generation: jointly predicting the notes $\it and$ $\it also$ their expressive timing and dynamics. We consider the significance and qualities of the data set needed for this. Having identified both a problem domain and characteristics of an appropriate data set, we show an LSTM-based recurrent network model that subjectively performs quite well on this task. Critically, we provide generated examples. We also include feedback from professional composers and musicians about some of these examples.

Includes members:
  Member "morpheus generating structured music with constrained patterns and tension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a functional taxonomy of music generation systems")
  Member "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "music generation by deep learning challenges and directions")
  Member "a functional taxonomy of music generation systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "ddsp differentiable digital signal processing")
  Member "getting closer to the essence of music the con espressione manifesto":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards explainable music emotion recognition the route via mid level features")
  Member "neural translation of musical style":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "supervised symbolic music style translation using synthetic data")
  Member "maximum entropy models for generation of expressive music":
    also connected through other hyperedges:
      (none)
  Member "wavenet a generative model for raw audio":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "noise estimation using density estimation for self supervised multimodal 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)