human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "pi vae encoding stochastic process priors with variational autoencoders"):
  Abstract: Stochastic processes provide a mathematically elegant way model complex data. In theory, they provide flexible priors over function classes that can encode a wide range of interesting assumptions. In practice, however, efficient inference by optimisation or marginalisation is difficult, a problem further exacerbated with big data and high dimensional input spaces. We propose a novel variational autoencoder (VAE) called the prior encoding variational autoencoder ($\pi$VAE). The $\pi$VAE is finitely exchangeable and Kolmogorov consistent, and thus is a continuous stochastic process. We use $\pi$VAE to learn low dimensional embeddings of function classes. We show that our framework can accurately learn expressive function classes such as Gaussian processes, but also properties of functions to enable statistical inference (such as the integral of a log Gaussian process). For popular tasks, such as spatial interpolation, $\pi$VAE achieves state-of-the-art performance both in terms of accuracy and computational efficiency. Perhaps most usefully, we demonstrate that the low dimensional independently distributed latent space representation learnt provides an elegant and scalable means of performing Bayesian inference for stochastic processes within probabilistic programming languages such as Stan.

Includes members:
  Member "an introduction to variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi supervised grasp detection by representation learning in a vector quantized latent space")
  Member "exact gaussian processes on a million data points":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian task embedding for few shot bayesian optimization")
  Member "quality of uncertainty quantification for bayesian neural network inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "subspace inference for bayesian deep learning")
  Member "attentive neural processes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "local nonparametric meta learning")
  Member "hamiltonian variational auto encoder":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy inspired models learning with sampler induced distributions")
  Member "conditional neural processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "local nonparametric meta learning")
  Member "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)