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 transcription modelling and composition using deep learning"
  Abstract: We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transcription modelling and composition. We build and train LSTM networks using approximately 23,000 music transcriptions expressed with a high-level vocabulary (ABC notation), and use them to generate new transcriptions. Our practical aim is to create music transcription models useful in particular contexts of music composition. We present results from three perspectives: 1) at the population level, comparing descriptive statistics of the set of training transcriptions and generated transcriptions; 2) at the individual level, examining how a generated transcription reflects the conventions of a music practice in the training transcriptions (Celtic folk); 3) at the application level, using the system for idea generation in music composition. We make our datasets, software and sound examples open and available: \url{this https URL}.

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"
    - "a functional taxonomy of music generation systems"

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 "character level recurrent neural networks in practice comparing training and sampling schemes"):
  with members:
    - "large scale user modeling with recurrent neural networks for music discovery on multiple time scales"
    - "lstm a search space odyssey"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "fast slow recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "improved recurrent neural networks for session based recommendations"
    - "multiplicative lstm for sequence modelling"

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"
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "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"

Connected through hyperedge HE-5 (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-6 (induced by source paper "improving automatic jazz melody generation by transfer learning techniques"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "transfer learning for music classification and regression tasks"
    - "representation learning of music using artist labels"
    - "what makes imagenet good for transfer learning"
    - "song from pi a musically plausible network for pop music generation"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-7 (induced by source paper "deep learning based automatic downbeat tracking a brief review"):
  with members:
    - "a universal music translation network"
    - "madmom a new python audio and music signal processing library"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "robust downbeat tracking using an ensemble of convolutional networks"
    - "eesen end to end speech recognition using deep rnn models and wfst based decoding"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "improvements to deep convolutional neural networks for lvcsr"

Connected through hyperedge HE-8 (induced by source paper "progressive generative adversarial binary networks for music generation"):
  with members:
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "progressive growing of gans for improved quality stability and variation"
    - "improved training of wasserstein gans"
    - "song from pi a musically plausible network for pop music generation"
    - "hierarchical multiscale recurrent neural networks"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

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)