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 "infovae information maximizing variational autoencoders"):
  Abstract: A key advance in learning generative models is the use of amortized inference distributions that are jointly trained with the models. We find that existing training objectives for variational autoencoders can lead to inaccurate amortized inference distributions and, in some cases, improving the objective provably degrades the inference quality. In addition, it has been observed that variational autoencoders tend to ignore the latent variables when combined with a decoding distribution that is too flexible. We again identify the cause in existing training criteria and propose a new class of objectives (InfoVAE) that mitigate these problems. We show that our model can significantly improve the quality of the variational posterior and can make effective use of the latent features regardless of the flexibility of the decoding distribution. Through extensive qualitative and quantitative analyses, we demonstrate that our models outperform competing approaches on multiple performance metrics.

Includes members:
  Member "learning hierarchical features from generative models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks")
  Member "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive density estimation for generative models")
  Member "improved variational autoencoders for text modeling using dilated convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blank language models")
  Member "vae learning via stein variational gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "implicit deep latent variable models for text generation")
  Member "improved training of wasserstein gans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "improving variational inference with inverse autoregressive flow":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gradient boosted flows")
  Member "conditional image generation with pixelcnn decoders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic latent residual video prediction")
  Member "generating images with perceptual similarity metrics based on deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object centric image generation from layouts")

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)