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: "gaussian process prior variational autoencoders"
  Abstract: Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is the prior assumption that latent sample representations are independent and identically distributed. However, for many important datasets, such as time-series of images, this assumption is too strong: accounting for covariances between samples, such as those in time, can yield to a more appropriate model specification and improve performance in downstream tasks. In this work, we introduce a new model, the Gaussian Process (GP) Prior Variational Autoencoder (GPPVAE), to specifically address this issue. The GPPVAE aims to combine the power of VAEs with the ability to model correlations afforded by GP priors. To achieve efficient inference in this new class of models, we leverage structure in the covariance matrix, and introduce a new stochastic backpropagation strategy that allows for computing stochastic gradients in a distributed and low-memory fashion. We show that our method outperforms conditional VAEs (CVAEs) and an adaptation of standard VAEs in two image data applications.

Connected through hyperedge HE-1 (induced by source paper "gradient boosted flows"):
  with members:
    - "inference suboptimality in variational autoencoders"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "improving explorability in variational inference with annealed variational objectives"
    - "vae with a vampprior"
    - "improving variational auto encoders using householder flow"
    - "improving variational inference with inverse autoregressive flow"
    - "density estimation using real nvp"
    - "variational lossy autoencoder"

Connected through hyperedge HE-2 (induced by source paper "disentangling representations using gaussian processes in variational autoencoders for video prediction"):
  with members:
    - "bayes factor vae hierarchical bayesian deep auto encoder models for factor disentanglement"
    - "geometry of deep generative models for disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "latent space non linear statistics"
    - "disentangled sequential autoencoder"
    - "learning to decompose and disentangle representations for video prediction"
    - "decomposing motion and content for natural video sequence prediction"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-3 (induced by source paper "multi view stereo by temporal nonparametric fusion"):
  with members:
    - "unstructured multi view depth estimation using mask based multiplane representation"
    - "dpsnet end to end deep plane sweep stereo"
    - "deeptam deep tracking and mapping"
    - "deepmvs learning multi view stereopsis"
    - "mvdepthnet real time multiview depth estimation neural network"
    - "learning a multi view stereo machine"
    - "pivo probabilistic inertial visual odometry for occlusion robust navigation"
    - "demon depth and motion network for learning monocular stereo"

Connected through hyperedge HE-4 (induced by source paper "weakly supervised disentanglement by pairwise similarities"):
  with members:
    - "learning disentangled representations with reference based variational autoencoders"
    - "dual swap disentangling"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "information constraints on auto encoding variational bayes"
    - "multi view data generation without view supervision"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "multi level variational autoencoder learning disentangled representations from grouped observations"

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)