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 "a style transfer approach to source separation"):
  Abstract: Training neural networks for source separation involves presenting a mixture recording at the input of the network and updating network parameters in order to produce an output that resembles the clean source. Consequently, supervised source separation depends on the availability of paired mixture-clean training examples. In this paper, we interpret source separation as a style transfer problem. We present a variational auto-encoder network that exploits the commonality across the domain of mixtures and the domain of clean sounds and learns a shared latent representation across the two domains. Using these cycle-consistent variational auto-encoders, we learn a mapping from the mixture domain to the domain of clean sounds and perform source separation without explicitly supervising with paired training examples.

Includes members:
  Member "cyclegan vc2 improved cyclegan based non parallel voice conversion":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised any to many audiovisual synthesis via exemplar autoencoders")
  Member "unsupervised training of a deep clustering model for multichannel blind source separation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "two step sound source separation training on learned latent targets")
  Member "furcanet an end to end deep gated convolutional long short term memory deep neural networks for single channel speech separation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "la furca iterative context aware end to end monaural speech separation based on dual path deep parallel inter intra bi lstm with attention")
  Member "unsupervised training of neural mask based beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bootstrapping deep music separation from primitive auditory grouping principles")
  Member "audio style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "applying visual domain style transfer and texture synthesis techniques to audio insights and challenges")
  Member "stargan vc non parallel many to many voice conversion with star generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "voice conversion for whispered speech synthesis")
  Member "sdr half baked or well done":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "temporal spatial neural filter direction informed end to end multi channel target speech separation")
  Member "unsupervised deep clustering for source separation direct learning from mixtures using spatial information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "two step sound source separation training on learned latent targets")

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)