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: "neural translation of musical style"
  Abstract: Music is an expressive form of communication often used to convey emotion in scenarios where "words are not enough". Part of this information lies in the musical composition where well-defined language exists. However, a significant amount of information is added during a performance as the musician interprets the composition. The performer injects expressiveness into the written score through variations of different musical properties such as dynamics and tempo. In this paper, we describe a model that can learn to perform sheet music. Our research concludes that the generated performances are indistinguishable from a human performance, thereby passing a test in the spirit of a "musical Turing test".

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"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "exploring conditioning for generative music systems with human interpretable controls"):
  with members:
    - "piano genie"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "this time with feeling learning expressive musical performance"
    - "timbretron a wavenet cyclegan cqt audio pipeline for musical timbre transfer"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "music transformer"
    - "a functional taxonomy of music generation systems"

Connected through hyperedge HE-3 (induced by source paper "music performance analysis a survey"):
  with members:
    - "this time with feeling learning expressive musical performance"
    - "transfer learning for music classification and regression tasks"

Connected through hyperedge HE-4 (induced by source paper "symbolic music genre transfer with cyclegan"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "a universal music translation network"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "a functional taxonomy of music generation systems"
    - "jambot music theory aware chord based generation of polyphonic music with lstms"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep learning techniques for music generation a survey"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-5 (induced by source paper "midi vae modeling dynamics and instrumentation of music with applications to style transfer"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "jambot music theory aware chord based generation of polyphonic music with lstms"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "adversarially regularized autoencoders for generating discrete structures"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep learning techniques for music generation a survey"
    - "universal style transfer via feature transforms"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-6 (induced by source paper "music style transfer a position paper"):
  with members:
    - "counterpoint by convolution"
    - "neural style transfer for audio spectograms"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "deepj style specific music generation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "jade joint autoencoders for dis entanglement"
    - "deep learning techniques for music generation a survey"
    - "disentangled representations for manipulation of sentiment in text"

Connected through hyperedge HE-7 (induced by source paper "this time with feeling learning expressive musical performance"):
  with members:
    - "morpheus generating structured music with constrained patterns and tension"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "a functional taxonomy of music generation systems"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "getting closer to the essence of music the con espressione manifesto"
    - "maximum entropy models for generation of expressive music"
    - "wavenet a generative model for raw audio"
    - "music transcription modelling and composition using deep learning"

Connected through hyperedge HE-8 (induced by source paper "the nes music database a multi instrumental dataset with expressive performance attributes"):
  with members:
    - "counterpoint by convolution"
    - "deep learning techniques for music generation a survey"
    - "deepbach a steerable model for bach chorales generation"
    - "adam a method for stochastic optimization"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

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)