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: "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
  Abstract: We introduce a method for imposing higher-level structure on generated, polyphonic music. A Convolutional Restricted Boltzmann Machine (C-RBM) as a generative model is combined with gradient des- cent constraint optimisation to provide further control over the genera- tion process. Among other things, this allows for the use of a “template” piece, from which some structural properties can be extracted, and trans- ferred as constraints to the newly generated material. The sampling pro- cess is guided with Simulated Annealing to avoid local optima, and to find solutions that both satisfy the constraints, and are relatively stable with respect to the C-RBM. Results show that with this approach it is possible to control the higher-level self-similarity structure, the meter, and the tonal properties of the resulting musical piece, while preserving its local musical coherence.

Connected through hyperedge HE-1 (induced by source paper "music generation by deep learning challenges and directions"):
  with members:
    - "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"
    - "interactive music generation with positional constraints using anticipation rnns"

Connected through hyperedge HE-2 (induced by source paper "modeling musical structure with artificial neural networks"):
  with members:
    - "high level control of drum track generation using learned patterns of rhythmic interaction"
    - "learning complex basis functions for invariant representations of audio"
    - "counterpoint by convolution"
    - "a predictive model for music based on learned interval representations"
    - "improving content invariance in gated autoencoders for 2d and 3d object rotation"
    - "text based lstm networks for automatic music composition"
    - "deepbach a steerable model for bach chorales generation"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "continuous melody generation via disentangled short term representations and structural conditions"):
  with members:
    - "inspecting and interacting with meaningful music representations using vae"
    - "deep music analogy via latent representation disentanglement"
    - "learning to traverse latent spaces for musical score inpainting"
    - "the effect of explicit structure encoding of deep neural networks for symbolic music generation"
    - "a survey on dialogue systems recent advances and new frontiers"
    - "deep learning techniques for music generation a survey"
    - "wavenet a generative model for raw audio"
    - "deepbach a steerable model for bach chorales 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"
    - "this time with feeling learning expressive musical performance"
    - "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 "counterpoint by convolution"):
  with members:
    - "neural autoregressive distribution estimation"
    - "style imitation and chord invention in polyphonic music with exponential families"
    - "wavenet a generative model for raw audio"
    - "deepbach a steerable model for bach chorales generation"
    - "pixel recurrent neural networks"
    - "a neural algorithm of artistic style"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "high level control of drum track generation using learned patterns of rhythmic interaction"):
  with members:
    - "audio style transfer"
    - "deepj style specific music generation"
    - "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-7 (induced by source paper "morpheus generating structured music with constrained patterns and tension"):
  with members:
    - "a functional taxonomy of music generation systems"
    - "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-8 (induced by source paper "the effect of explicit structure encoding of deep neural networks for symbolic music generation"):
  with members:
    - "counterpoint by convolution"
    - "neural style transfer for audio spectograms"
    - "music style transfer issues a position paper"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "neural audio synthesis of musical notes with wavenet autoencoders"
    - "deep learning techniques for music generation a survey"
    - "a unit selection methodology for music generation using deep neural networks"
    - "fast wavenet generation algorithm"

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)