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 "exploring conditioning for generative music systems with human interpretable controls"):
  Abstract: Performance RNN is a machine-learning system designed primarily for the generation of solo piano performances using an event-based (rather than audio) representation. More specifically, Performance RNN is a long short-term memory (LSTM) based recurrent neural network that models polyphonic music with expressive timing and dynamics (Oore et al., 2018). The neural network uses a simple language model based on the Musical Instrument Digital Interface (MIDI) file format. Performance RNN is trained on the e-Piano Junior Competition Dataset (International Piano e-Competition, 2018), a collection of solo piano performances by expert pianists. As an artistic tool, one of the limitations of the original model has been the lack of useable controls. The standard form of Performance RNN can generate interesting pieces, but little control is provided over what specifically is generated. This paper explores a set of conditioning-based controls used to influence the generation process.

Includes members:
  Member "piano genie":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for audio signal processing")
  Member "the challenge of realistic music generation modelling raw audio at scale":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical autoregressive image models with auxiliary decoders")
  Member "this time with feeling learning expressive musical performance":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dawson a domain adaptive few shot generation framework")
  Member "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for audio signal processing")
  Member "a hierarchical latent vector model for learning long term structure in music":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "music generation by deep learning challenges and directions")
  Member "music transformer":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stand alone self attention in vision models")
  Member "a functional taxonomy of music generation systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "music generation by deep learning challenges and directions")
  Member "neural translation of musical style":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "supervised symbolic music style translation using synthetic data")

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)