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: "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
  Abstract: We introduce MIDI-VAE, a neural network model based on Variational Autoencoders that is capable of handling polyphonic music with multiple instrument tracks, as well as modeling the dynamics of music by incorporating note durations and velocities. We show that MIDI-VAE can perform style transfer on symbolic music by automatically changing pitches, dynamics and instruments of a music piece from, e.g., a Classical to a Jazz style. We evaluate the efficacy of the style transfer by training separate style validation classifiers. Our model can also interpolate between short pieces of music, produce medleys and create mixtures of entire songs. The interpolations smoothly change pitches, dynamics and instrumentation to create a harmonic bridge between two music pieces. To the best of our knowledge, this work represents the first successful attempt at applying neural style transfer to complete musical compositions.

Connected through hyperedge HE-1 (induced by source paper "learning style aware symbolic music representations by adversarial autoencoders"):
  with members:
    - "a hierarchical latent vector model for learning long term structure in music"
    - "toward controlled generation of text"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "style transfer from non parallel text by cross alignment"
    - "demystifying neural style transfer"
    - "improved training of wasserstein gans"
    - "neural style transfer a review"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-2 (induced by source paper "learning a latent space of style aware symbolic music representations by adversarial autoencoders"):
  with members:
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "demystifying neural style transfer"
    - "improved training of wasserstein gans"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-3 (induced by source paper "learning disentangled representations of timbre and pitch for musical instrument sounds using gaussian mixture variational autoencoders"):
  with members:
    - "modulated variational auto encoders for many to many musical timbre transfer"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "learning disentangled representations for timber and pitch in music audio"
    - "hierarchical generative modeling for controllable speech synthesis"
    - "a universal music translation network"
    - "toward controlled generation of text"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-4 (induced by source paper "learning to traverse latent spaces for musical score inpainting"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "this time with feeling learning expressive musical performance"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "the machine learning algorithm as creative musical tool"
    - "a predictive model for music based on learned interval representations"
    - "music transformer"
    - "audio to score alignment using transposition invariant features"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

Connected through hyperedge HE-5 (induced by source paper "supervised symbolic music style translation using synthetic data"):
  with members:
    - "audio style transfer"
    - "cover song synthesis by analogy"
    - "music style transfer a position paper"
    - "symbolic music genre transfer with cyclegan"
    - "adversarially regularized autoencoders"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning from synthetic humans"

Connected through hyperedge HE-6 (induced by source paper "deep music analogy via latent representation disentanglement"):
  with members:
    - "inspecting and interacting with meaningful music representations using vae"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "voice impersonation using generative adversarial networks"
    - "disentangled sequential autoencoder"
    - "cover song synthesis by analogy"
    - "music style transfer a position paper"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "demonstration of performancenet a convolutional neural network model for score to audio music generation"):
  with members:
    - "gansynth adversarial neural audio synthesis"
    - "neural music synthesis for flexible timbre control"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "performancenet score to audio music generation with multi band convolutional residual network"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-8 (induced by source paper "midi sandwich2 rnn based hierarchical multi modal fusion generation vae networks for multi track symbolic music generation"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "symbolic music genre transfer with cyclegan"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "improved training of wasserstein gans"
    - "deep learning techniques for music generation a survey"

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)