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 variable modelling with hyperbolic normalizing flows"):
  Abstract: The choice of approximate posterior distributions plays a central role in stochastic variational inference (SVI). One effective solution is the use of normalizing flows \cut{defined on Euclidean spaces} to construct flexible posterior distributions. However, one key limitation of existing normalizing flows is that they are restricted to the Euclidean space and are ill-equipped to model data with an underlying hierarchical structure. To address this fundamental limitation, we present the first extension of normalizing flows to hyperbolic spaces. We first elevate normalizing flows to hyperbolic spaces using coupling transforms defined on the tangent bundle, termed Tangent Coupling ($\mathcal{TC}$). We further introduce Wrapped Hyperboloid Coupling ($\mathcal{W}\mathbb{H}C$), a fully invertible and learnable transformation that explicitly utilizes the geometric structure of hyperbolic spaces, allowing for expressive posteriors while being efficient to sample from. We demonstrate the efficacy of our novel normalizing flow over hyperbolic VAEs and Euclidean normalizing flows. Our approach achieves improved performance on density estimation, as well as reconstruction of real-world graph data, which exhibit a hierarchical structure. Finally, we show that our approach can be used to power a generative model over hierarchical data using hyperbolic latent variables.

Includes members:
  Member "adversarial autoencoders with constant curvature latent manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for molecular design a review of the state of the art")
  Member "riemannian normalizing flow on variational wasserstein autoencoder for text modeling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "effective estimation of deep generative language models")
  Member "graph normalizing flows":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "mixed curvature variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational autoencoders with riemannian brownian motion priors")
  Member "poincare wasserstein autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "constant curvature graph convolutional networks")
  Member "hyperbolic graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constant curvature graph convolutional networks")
  Member "hyperbolic image embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "metric learning for dynamic text classification")
  Member "hyperbolic graph convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bilinear graph neural network with node interactions")

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)