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: "deepj style specific music generation"
  Abstract: Recent advances in deep neural networks have enabled algorithms to compose music that is comparable to music composed by humans. However, few algorithms allow the user to generate music with tunable parameters. The ability to tune properties of generated music will yield more practical benefits for aiding artists, filmmakers, and composers in their creative tasks. In this paper, we introduce DeepJ - an end-to-end generative model that is capable of composing music conditioned on a specific mixture of composer styles. Our innovations include methods to learn musical style and music dynamics. We use our model to demonstrate a simple technique for controlling the style of generated music as a proof of concept. Evaluation of our model using human raters shows that we have improved over the Biaxial LSTM approach.

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "lakhnes improving multi instrumental music generation with cross domain pre training"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "counterpoint by convolution"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "enabling factorized piano music modeling and generation with the maestro dataset"
    - "the nes music database a multi instrumental dataset with expressive performance attributes"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "high level control of drum track generation using learned patterns of rhythmic interaction"):
  with members:
    - "audio style transfer"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "lead sheet generation and arrangement by conditional generative adversarial network"
    - "learning transposition invariant interval features from symbolic music and audio"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "a predictive model for music based on learned interval representations"
    - "self normalizing neural networks"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-4 (induced by source paper "modeling melodic feature dependency with modularized variational auto encoder"):
  with members:
    - "bachprop learning to compose music in multiple styles"
    - "convolutional generative adversarial networks with binary neurons for polyphonic music generation"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "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"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "samplernn an unconditional end to end neural audio generation model"

Connected through hyperedge HE-5 (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"
    - "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"
    - "neural translation of musical style"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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)