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 "progressive generative adversarial binary networks for music generation"):
  Abstract: Recent improvements in generative adversarial network (GAN) training techniques prove that progressively training a GAN drastically stabilizes the training and improves the quality of outputs produced. Adding layers after the previous ones have converged has proven to help in better overall convergence and stability of the model as well as reducing the training time by a sufficient amount. Thus we use this training technique to train the model progressively in the time and pitch domain i.e. starting from a very small time value and pitch range we gradually expand the matrix sizes until the end result is a completely trained model giving outputs having tensor sizes [4 (bar) x 96 (time steps) x 84 (pitch values) x 8 (tracks)]. As proven in previously proposed models deterministic binary neurons also help in improving the results. Thus we make use of a layer of deterministic binary neurons at the end of the generator to get binary valued outputs instead of fractional values existing between 0 and 1.

Includes members:
  Member "convolutional generative adversarial networks with binary neurons for polyphonic music generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dawson a domain adaptive few shot generation framework")
  Member "midinet a convolutional generative adversarial network for symbolic domain music generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "music generation by deep learning challenges and directions")
  Member "progressive growing of gans for improved quality stability and variation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "song from pi a musically plausible network for pop music generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "rl duet online music accompaniment generation using deep reinforcement learning")
  Member "music transcription modelling and composition using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "music generation by deep learning challenges and directions")
  Member "hierarchical multiscale recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "estimating or propagating gradients through stochastic neurons for conditional computation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention over parameters for dialogue systems")

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)