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: "sampling variations of lead sheets"
  Abstract: Machine-learning techniques have been recently used with spectacular results to generate artefacts such as music or text. However, these techniques are still unable to capture and generate artefacts that are convincingly structured. In this paper we present an approach to generate structured musical sequences. We introduce a mechanism for sampling efficiently variations of musical sequences. Given a input sequence and a statistical model, this mechanism samples a set of sequences whose distance to the input sequence is approximately within specified bounds. This mechanism is implemented as an extension of belief propagation, and uses local fields to bias the generation. We show experimentally that sampled sequences are indeed closely correlated to the standard musical similarity measure defined by Mongeau and Sankoff. We then show how this mechanism can used to implement composition strategies that enforce arbitrary structure on a musical lead sheet generation problem.

Connected through hyperedge HE-1 (induced by source paper "rhythm chord and melody generation for lead sheets using recurrent neural networks"):
  with members:
    - "lead sheet generation and arrangement by conditional generative adversarial network"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "chord generation from symbolic melody using blstm networks"
    - "deep learning techniques for music generation a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "lead sheet generation and arrangement by conditional generative adversarial network"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "semi recurrent cnn based vae gan for sequential data generation"
    - "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"
    - "chord generation from symbolic melody using blstm networks"
    - "improved training of wasserstein gans"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-3 (induced by source paper "deep rank based transposition invariant distances on musical sequences"):
  with members:
    - "harmonic networks deep translation and rotation equivariance"
    - "group equivariant convolutional networks"
    - "very deep convolutional networks for large scale image recognition"
    - "sequence to sequence learning with neural networks"

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)