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: "stochastic wavenet a generative latent variable model for sequential data"
  Abstract: How to model distribution of sequential data, including but not limited to speech and human motions, is an important ongoing research problem. It has been demonstrated that model capacity can be significantly enhanced by introducing stochastic latent variables in the hidden states of recurrent neural networks. Simultaneously, WaveNet, equipped with dilated convolutions, achieves astonishing empirical performance in natural speech generation task. In this paper, we combine the ideas from both stochastic latent variables and dilated convolutions, and propose a new architecture to model sequential data, termed as Stochastic WaveNet, where stochastic latent variables are injected into the WaveNet structure. We argue that Stochastic WaveNet enjoys powerful distribution modeling capacity and the advantage of parallel training from dilated convolutions. In order to efficiently infer the posterior distribution of the latent variables, a novel inference network structure is designed based on the characteristics of WaveNet architecture. State-of-the-art performances on benchmark datasets are obtained by Stochastic WaveNet on natural speech modeling and high quality human handwriting samples can be generated as well.

Connected through hyperedge HE-1 (induced by source paper "stcn stochastic temporal convolutional networks"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "fast decoding in sequence models using discrete latent variables"
    - "deepwriting making digital ink editable via deep generative modeling"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "filtering variational objectives"
    - "learning hierarchical features from generative models"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-2 (induced by source paper "cyclical annealing schedule a simple approach to mitigating kl vanishing"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "isolating sources of disentanglement in variational autoencoders"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"
    - "latent intention dialogue models"
    - "snapshot ensembles train 1 get m for free"
    - "infovae information maximizing variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "re examination of the role of latent variables in sequence modeling"):
  with members:
    - "stcn stochastic temporal convolutional networks"
    - "transformer xl attentive language models beyond a fixed length context"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "pixelsnail an improved autoregressive generative model"
    - "sgdr stochastic gradient descent with restarts"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"

Connected through hyperedge HE-4 (induced by source paper "semi implicit stochastic recurrent neural networks"):
  with members:
    - "condition transforming variational autoencoder for conversation response generation"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "twin networks matching the future for sequence generation"
    - "neural autoregressive distribution estimation"
    - "an architecture for deep hierarchical generative models"
    - "pixelvae a latent variable model for natural images"

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.LG(Machine Learning)