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: "jambot music theory aware chord based generation of polyphonic music with lstms"
  Abstract: We propose a novel approach for the generation of polyphonic music based on LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord progression based on a chord embedding. A second LSTM then generates polyphonic music from the predicted chord progression. The generated music sounds pleasing and harmonic, with only few dissonant notes. It has clear long-term structure that is similar to what a musician would play during a jam session. We show that our approach is sensible from a music theory perspective by evaluating the learned chord embeddings. Surprisingly, our simple model managed to extract the circle of fifths, an important tool in music theory, from the dataset.

Connected through hyperedge HE-1 (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"
    - "a functional taxonomy of music generation systems"
    - "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-2 (induced by source paper "midi vae modeling dynamics and instrumentation of music with applications to style transfer"):
  with members:
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "adversarially regularized autoencoders for generating discrete structures"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep learning techniques for music generation a survey"
    - "neural translation of musical style"
    - "universal style transfer via feature transforms"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-3 (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"
    - "deepj style specific 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"
    - "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-4 (induced by source paper "deep segment hash learning for music generation"):
  with members:
    - "a unit selection methodology for music generation using deep neural networks"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "deep cross modal hashing"
    - "song from pi a musically plausible network for pop music generation"
    - "deepbach a steerable model for bach chorales generation"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

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)