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: "a functional taxonomy of music generation systems"
  Abstract: Digital advances have transformed the face of automatic music generation since its beginnings at the dawn of computing. Despite the many breakthroughs, issues such as the musical tasks targeted by different machines and the degree to which they succeed remain open questions. We present a functional taxonomy for music generation systems with reference to existing systems. The taxonomy organizes systems according to the purposes for which they were designed. It also reveals the inter-relatedness amongst the systems. This design-centered approach contrasts with predominant methods-based surveys and facilitates the identification of grand challenges to set the stage for new breakthroughs.

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"
    - "music style transfer issues a position paper"
    - "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"
    - "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"
    - "music style transfer issues a position paper"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "the bach doodle approachable music composition with machine learning at scale"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "counterpoint by convolution"
    - "deep learning techniques for music generation a survey"
    - "neural autoregressive distribution estimation"
    - "sequence tutor conservative fine tuning of sequence generation models with kl control"
    - "style imitation and chord invention in polyphonic music with exponential families"
    - "xception deep learning with depthwise separable convolutions"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (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"
    - "neural translation of musical style"

Connected through hyperedge HE-5 (induced by source paper "morpheus generating structured music with constrained patterns and tension"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "talking drums generating drum grooves with neural networks"
    - "ai methods in algorithmic composition a comprehensive survey"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-6 (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"
    - "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"
    - "neural translation of musical style"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-7 (induced by source paper "coupled recurrent models for polyphonic music composition"):
  with members:
    - "counterpoint by convolution"
    - "this time with feeling learning expressive musical performance"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "deep learning techniques for music generation a survey"
    - "modelling symbolic music beyond the piano roll"
    - "wavenet a generative model for raw audio"
    - "music transcription modelling and composition using deep learning"

Connected through hyperedge HE-8 (induced by source paper "singing voice separation using a deep convolutional neural network trained by ideal binary mask and cross entropy"):
  with members:
    - "jointly detecting and separating singing voice a multi task approach"
    - "an overview of lead and accompaniment separation in music"
    - "svsgan singing voice separation via generative adversarial network"
    - "exploiting saliency for object segmentation from image level labels"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"
    - "densely connected convolutional networks"
    - "singing voice separation and vocal f0 estimation based on mutual combination of robust principal component analysis and subharmonic summation"
    - "joint optimization of masks and deep recurrent neural networks for monaural 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)