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: "c rnn gan continuous recurrent neural networks with adversarial training"
  Abstract: Generative adversarial networks have been proposed as a way of efficiently training deep generative neural networks. We propose a generative adversarial model that works on continuous sequential data, and apply it by training it on a collection of classical music. We conclude that it generates music that sounds better and better as the model is trained, report statistics on generated music, and let the reader judge the quality by downloading the generated songs.

Connected through hyperedge HE-1 (induced by source paper "music generation by deep learning challenges and directions"):
  with members:
    - "imposing higher level structure in polyphonic music generation using convolutional restricted boltzmann machines and constraints"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "rl duet online music accompaniment generation using deep reinforcement learning"):
  with members:
    - "the bach doodle approachable music composition with machine learning at scale"
    - "counterpoint by convolution"
    - "gansynth adversarial neural audio synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "an actor critic algorithm for sequence prediction"

Connected through hyperedge HE-3 (induced by source paper "subadditivity of probability divergences on bayes nets with applications to time series gans"):
  with members:
    - "naomi non autoregressive multiresolution sequence imputation"
    - "mad gan multivariate anomaly detection for time series data with generative adversarial networks"
    - "adversarial video generation on complex datasets"
    - "gansynth adversarial neural audio synthesis"
    - "adversarial audio synthesis"
    - "improved training of wasserstein gans"
    - "on the discrimination generalization tradeoff in gans"
    - "square hellinger subadditivity for bayesian networks and its applications to identity testing"

Connected through hyperedge HE-4 (induced by source paper "learning style aware symbolic music representations by adversarial autoencoders"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "toward controlled generation of text"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "style transfer from non parallel text by cross alignment"
    - "demystifying neural style transfer"
    - "improved training of wasserstein gans"
    - "neural style transfer a review"

Connected through hyperedge HE-5 (induced by source paper "learning a latent space of style aware symbolic music representations by adversarial autoencoders"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "demystifying neural style transfer"
    - "improved training of wasserstein gans"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-6 (induced by source paper "controlled time series generation for automotive software in the loop testing using gans"):
  with members:
    - "quick and easy time series generation with established image based gans"
    - "sensegen a deep learning architecture for synthetic sensor data generation"
    - "improved techniques for training gans"
    - "an overview of gradient descent optimization algorithms"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "autoencoding beyond pixels using a learned similarity metric"

Connected through hyperedge HE-7 (induced by source paper "differentially private mixed type data generation for unsupervised learning"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "differentially private learning with adaptive clipping"
    - "differentially private model publishing for deep learning"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "private selection from private candidates"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "generating artificial data for private deep learning"

Connected through hyperedge HE-8 (induced by source paper "adversarial symmetric gans bridging adversarial samples and adversarial networks"):
  with members:
    - "bridging adversarial robustness and gradient interpretability"
    - "spectral normalization for generative adversarial networks"
    - "on convergence and stability of gans"
    - "generating adversarial examples with adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "ape gan adversarial perturbation elimination with gan"
    - "wasserstein divergence for gans"
    - "unpaired image to image translation using cycle consistent adversarial 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)