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 "latent space non linear statistics"):
  Abstract: Given data, deep generative models, such as variational autoencoders (VAE) and generative adversarial networks (GAN), train a lower dimensional latent representation of the data space. The linear Euclidean geometry of data space pulls back to a nonlinear Riemannian geometry on the latent space. The latent space thus provides a low-dimensional nonlinear representation of data and classical linear statistical techniques are no longer applicable. In this paper we show how statistics of data in their latent space representation can be performed using techniques from the field of nonlinear manifold statistics. Nonlinear manifold statistics provide generalizations of Euclidean statistical notions including means, principal component analysis, and maximum likelihood fits of parametric probability distributions. We develop new techniques for maximum likelihood inference in latent space, and adress the computational complexity of using geometric algorithms with high-dimensional data by training a separate neural network to approximate the Riemannian metric and cometric tensor capturing the shape of the learned data manifold.

Includes members:
  Member "a geometric framework for stochastic shape analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "string methods for stochastic image and shape matching")
  Member "differential geometry and stochastic dynamics with deep learning numerics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "horizontal flows and manifold stochastics in geometric deep learning")
  Member "bridge simulation and metric estimation on landmark manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a geometric framework for stochastic shape analysis")
  Member "the riemannian geometry of deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on implicit regularization in beta vaes")
  Member "theano a python framework for fast computation of mathematical expressions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "a riemannian network for spd matrix learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep heterogeneous hashing for face video retrieval")

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.CV(Computer Vision and Pattern Recognition)