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: "this time with feeling learning expressive musical performance"
  Abstract: Music generation has generally been focused on either creating scores or interpreting them. We discuss differences between these two problems and propose that, in fact, it may be valuable to work in the space of direct $\it performance$ generation: jointly predicting the notes $\it and$ $\it also$ their expressive timing and dynamics. We consider the significance and qualities of the data set needed for this. Having identified both a problem domain and characteristics of an appropriate data set, we show an LSTM-based recurrent network model that subjectively performs quite well on this task. Critically, we provide generated examples. We also include feedback from professional composers and musicians about some of these examples.

Connected through hyperedge HE-1 (induced by source paper "dawson a domain adaptive few shot generation framework"):
  with members:
    - "metalgan multi domain label less image synthesis using cgans and meta learning"
    - "figr few shot image generation with reptile"
    - "counterpoint by convolution"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "sample efficient adaptive text to speech"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "variational memory addressing in generative models"
    - "online learning with gated linear networks"

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"
    - "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"
    - "a functional taxonomy of music generation systems"

Connected through hyperedge HE-3 (induced by source paper "pop music transformer generating music with rhythm and harmony"):
  with members:
    - "adversarial learning for improved onsets and frames music transcription"
    - "encoding musical style with transformer autoencoders"
    - "lakhnes improving multi instrumental music generation with cross domain pre training"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

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"
    - "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"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"

Connected through hyperedge HE-5 (induced by source paper "encoding musical style with transformer autoencoders"):
  with members:
    - "exploring conditioning for generative music systems with human interpretable controls"
    - "learning to groove with inverse sequence transformations"
    - "improving automatic jazz melody generation by transfer learning techniques"
    - "counterpoint by convolution"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "self attention with relative position representations"
    - "discrete autoencoders for sequence models"
    - "a hierarchical latent vector model for learning long term structure in music"

Connected through hyperedge HE-6 (induced by source paper "learning to groove with inverse sequence transformations"):
  with members:
    - "counterpoint by convolution"
    - "video to video synthesis"
    - "predicting expressive speaking style from text in end to end speech synthesis"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "style tokens unsupervised style modeling control and transfer in end to end speech synthesis"
    - "hierarchical neural story generation"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "semi supervised training for improving data efficiency in end to end speech synthesis"

Connected through hyperedge HE-7 (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"
    - "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"
    - "neural translation of musical style"

Connected through hyperedge HE-8 (induced by source paper "computer assisted composition in continuous time"):
  with members:
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "symbolic music data version 1 0"
    - "computer assisted composition with recurrent neural networks"
    - "deepbach a steerable model for bach chorales generation"
    - "ai methods in algorithmic composition a comprehensive survey"

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)