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: "style imitation and chord invention in polyphonic music with exponential families"
  Abstract: Modeling polyphonic music is a particularly challenging task because of the intricate interplay between melody and harmony. A good model should satisfy three requirements: statistical accuracy (capturing faithfully the statistics of correlations at various ranges, horizontally and vertically), flexibility (coping with arbitrary user constraints), and generalization capacity (inventing new material, while staying in the style of the training corpus). Models proposed so far fail on at least one of these requirements. We propose a statistical model of polyphonic music, based on the maximum entropy principle. This model is able to learn and reproduce pairwise statistics between neighboring note events in a given corpus. The model is also able to invent new chords and to harmonize unknown melodies. We evaluate the invention capacity of the model by assessing the amount of cited, re-discovered, and invented chords on a corpus of Bach chorales. We discuss how the model enables the user to specify and enforce user-defined constraints, which makes it useful for style-based, interactive music generation.

Connected through hyperedge HE-1 (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-2 (induced by source paper "counterpoint by convolution"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "neural autoregressive distribution estimation"
    - "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-3 (induced by source paper "the bach doodle approachable music composition with machine learning at scale"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "counterpoint by convolution"
    - "a functional taxonomy of music generation systems"
    - "deep learning techniques for music generation a survey"
    - "neural autoregressive distribution estimation"
    - "sequence tutor conservative fine tuning of sequence generation models with kl control"
    - "xception deep learning with depthwise separable convolutions"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-4 (induced by source paper "music transformer"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "this time with feeling learning expressive musical performance"
    - "self attention with relative position representations"
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "neural autoregressive distribution estimation"
    - "a decomposable attention model for natural language inference"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-5 (induced by source paper "bandnet a neural network based multi instrument beatles style midi music composition machine"):
  with members:
    - "counterpoint by convolution"
    - "song from pi a musically plausible network for pop music generation"
    - "deepbach a steerable model for bach chorales generation"
    - "adam a method for stochastic optimization"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-6 (induced by source paper "an energy based generative sequence model for testing sensory theories of western harmony"):
  with members:
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"

Connected through hyperedge HE-7 (induced by source paper "interactive music generation with positional constraints using anticipation rnns"):
  with members:
    - "automated curriculum learning for neural networks"
    - "music transcription modelling and composition using deep learning"
    - "deepbach a steerable model for bach chorales generation"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "the burbea rao and bhattacharyya centroids"

Connected through hyperedge HE-8 (induced by source paper "deepbach a steerable model for bach chorales generation"):
  with members:
    - "ensuring rapid mixing and low bias for asynchronous gibbs sampling"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning longer memory in recurrent neural networks"
    - "constructing long short term memory based deep recurrent neural networks for large vocabulary speech recognition"
    - "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.AI(Artificial Intelligence)