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 "mixed curvature variational autoencoders"):
  Abstract: It has been shown that using geometric spaces with non-zero curvature instead of plain Euclidean spaces with zero curvature improves performance on a range of Machine Learning tasks for learning representations. Recent work has leveraged these geometries to learn latent variable models like Variational Autoencoders (VAEs) in spherical and hyperbolic spaces with constant curvature. While these approaches work well on particular kinds of data that they were designed for e.g. tree-like data for a hyperbolic VAE, there exists no generic approach unifying all three models. We develop a Mixed-curvature Variational Autoencoder, an efficient way to train a VAE whose latent space is a product of constant curvature Riemannian manifolds, where the per-component curvature can be learned. This generalizes the Euclidean VAE to curved latent spaces, as the model essentially reduces to the Euclidean VAE if curvatures of all latent space components go to 0.

Includes members:
  Member "constant curvature graph convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hyperbolic graph attention network")
  Member "riemannian adaptive optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient riemannian optimization on the stiefel manifold via the cayley transform")
  Member "neural autoregressive flows":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "woodbury transformations for deep generative flows")
  Member "challenging common assumptions in the unsupervised learning of disentangled representations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nestedvae isolating common factors via weak supervision")
  Member "learning continuous hierarchies in the lorentz model of hyperbolic geometry":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "graphvae towards generation of small graphs using variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "hyperbolic neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "data augmentation with mobius transformations")
  Member "spherical latent spaces for stable variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "paraphrase generation with latent bag of words")

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)