human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "singing style transfer using cycle consistent boundary equilibrium generative adversarial networks"
  Abstract: Can we make a famous rap singer like Eminem sing whatever our favorite song? Singing style transfer attempts to make this possible, by replacing the vocal of a song from the source singer to the target singer. This paper presents a method that learns from unpaired data for singing style transfer using generative adversarial networks.

Connected through hyperedge HE-1 (induced by source paper "performancenet score to audio music generation with multi band convolutional residual network"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "frame level instrument recognition by timbre and pitch"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "conditioning deep generative raw audio models for structured automatic music"
    - "lead sheet generation and arrangement by conditional generative adversarial network"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "progressive growing of gans for improved quality stability and variation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

Connected through hyperedge HE-2 (induced by source paper "play as you like timbre enhanced multi modal music style transfer"):
  with members:
    - "neural style transfer for audio spectograms"
    - "the relativistic discriminator a key element missing from standard gan"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "multimodal unsupervised image to image translation"
    - "conditional end to end audio transforms"
    - "music style transfer issues a position paper"
    - "a universal music translation network"
    - "generative face completion"

Connected through hyperedge HE-3 (induced by source paper "denoising auto encoder with recurrent skip connections and residual regression for music source separation"):
  with members:
    - "frame level instrument recognition by timbre and pitch"
    - "an overview of lead and accompaniment separation in music"
    - "mmdenselstm an efficient combination of convolutional and recurrent neural networks for audio source separation"
    - "mad twinnet masker denoiser architecture with twin networks for monaural sound source separation"
    - "vocal melody extraction using patch based cnn"
    - "music source separation using stacked hourglass networks"
    - "transcribing lyrics from commercial song audio the first step towards singing content processing"
    - "multi scale multi band densenets for audio source separation"

Question: Please tell me which class the center node 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)