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: "symbolic music genre transfer with cyclegan"
  Abstract: Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have recently been applied to style and domain transfer for images, and in the case of VAEs, music. GAN-based models employing several generators and some form of cycle consistency loss have been among the most successful for image domain transfer. In this paper we apply such a model to symbolic music and show the feasibility of our approach for music genre transfer. Evaluations using separate genre classifiers show that the style transfer works well. In order to improve the fidelity of the transformed music, we add additional discriminators that cause the generators to keep the structure of the original music mostly intact, while still achieving strong genre transfer. Visual and audible results further show the potential of our approach. To the best of our knowledge, this paper represents the first application of GANs to symbolic music domain transfer.

Connected through hyperedge HE-1 (induced by source paper "supervised symbolic music style translation using synthetic data"):
  with members:
    - "audio style transfer"
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "cover song synthesis by analogy"
    - "music style transfer a position paper"
    - "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-2 (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"
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "improved training of wasserstein gans"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-3 (induced by source paper "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"):
  with members:
    - "neural style transfer for audio spectograms"
    - "modulated variational auto encoders for many to many musical timbre transfer"
    - "augmented cyclic adversarial learning for low resource domain adaptation"
    - "a multi discriminator cyclegan for unsupervised non parallel speech domain adaptation"
    - "music style transfer issues a position paper"
    - "a universal music translation network"
    - "unsupervised cipher cracking using discrete gans"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"

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)