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: "music style transfer issues a position paper"
  Abstract: Led by the success of neural style transfer on visual arts, there has been a rising trend very recently in the effort of music style transfer. However, "music style" is not yet a well-defined concept from a scientific point of view. The difficulty lies in the intrinsic multi-level and multi-modal character of music representation (which is very different from image representation). As a result, depending on their interpretation of "music style", current studies under the category of "music style transfer", are actually solving completely different problems that belong to a variety of sub-fields of Computer Music. Also, a vanilla end-to-end approach, which aims at dealing with all levels of music representation at once by directly adopting the method of image style transfer, leads to poor results. Thus, we see a vital necessity to re-define music style transfer more precisely and scientifically based on the uniqueness of music representation, as well as to connect different aspects of music style transfer with existing well-established sub-fields of computer music studies. Otherwise, an accumulated upcoming literature (all named after music style transfer) will lead to a great confusion of the underlying problems as well as negligence of the treasures in computer music before the age of deep learning. In addition, we discuss the current limitations of music style modeling and its future directions by drawing spirit from some deep generative models, especially the ones using unsupervised learning and disentanglement techniques.

Connected through hyperedge HE-1 (induced by source paper "music generation by deep learning challenges and directions"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "the machine learning algorithm as creative musical tool"
    - "music style transfer a position paper"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "a functional taxonomy of music generation systems"
    - "interactive music generation with positional constraints using anticipation rnns"

Connected through hyperedge HE-2 (induced by source paper "mg vae deep chinese folk songs generation with specific regional styles"):
  with members:
    - "disentangled representation learning for non parallel text style transfer"
    - "this time with feeling learning expressive musical performance"
    - "neural melody composition from lyrics"
    - "deepj style specific music generation"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "learning disentangled representations for timber and pitch in music audio"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "a functional taxonomy of music generation systems"

Connected through hyperedge HE-3 (induced by source paper "inspecting and interacting with meaningful music representations using vae"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (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"
    - "a universal music translation network"
    - "unsupervised cipher cracking using discrete gans"
    - "symbolic music genre transfer with cyclegan"
    - "comparison of time frequency representations for environmental sound classification using convolutional neural networks"

Connected through hyperedge HE-5 (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"
    - "singing style transfer using cycle consistent boundary equilibrium generative adversarial networks"
    - "a universal music translation network"
    - "generative face completion"

Connected through hyperedge HE-6 (induced by source paper "the effect of explicit structure encoding of deep neural networks for symbolic music generation"):
  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"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "deep learning techniques for music generation a survey"
    - "a unit selection methodology for music generation using deep neural networks"
    - "fast wavenet generation algorithm"

Connected through hyperedge HE-7 (induced by source paper "deep learning techniques for music generation a survey"):
  with members:
    - "music generation by deep learning challenges and directions"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "conditioning deep generative raw audio models for structured automatic music"
    - "deepj style specific music generation"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "the machine learning algorithm as creative musical tool"
    - "music style transfer a position paper"
    - "music transformer"

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)