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 "don t blame the elbo a linear vae perspective on posterior collapse"):
  Abstract: Posterior collapse in Variational Autoencoders (VAEs) arises when the variational posterior distribution closely matches the prior for a subset of latent variables. This paper presents a simple and intuitive explanation for posterior collapse through the analysis of linear VAEs and their direct correspondence with Probabilistic PCA (pPCA). We explain how posterior collapse may occur in pPCA due to local maxima in the log marginal likelihood. Unexpectedly, we prove that the ELBO objective for the linear VAE does not introduce additional spurious local maxima relative to log marginal likelihood. We show further that training a linear VAE with exact variational inference recovers an identifiable global maximum corresponding to the principal component directions. Empirically, we find that our linear analysis is predictive even for high-capacity, non-linear VAEs and helps explain the relationship between the observation noise, local maxima, and posterior collapse in deep Gaussian VAEs.

Includes members:
  Member "lagging inference networks and posterior collapse in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "diagnosing and enhancing vae models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ae ot gan training gans from data specific latent distribution")
  Member "preventing posterior collapse with delta vaes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "loss landscapes of regularized linear autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a linear systems theory of normalizing flows")
  Member "automatic chemical design using a data driven continuous representation of molecules":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "isolating sources of disentanglement in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "inference suboptimality in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models")
  Member "variational autoencoders pursue pca directions by accident":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on implicit regularization in beta vaes")

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)