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 "theory and experiments on vector quantized autoencoders"):
  Abstract: Deep neural networks with discrete latent variables offer the promise of better symbolic reasoning, and learning abstractions that are more useful to new tasks. There has been a surge in interest in discrete latent variable models, however, despite several recent improvements, the training of discrete latent variable models has remained challenging and their performance has mostly failed to match their continuous counterparts. Recent work on vector quantized autoencoders (VQ-VAE) has made substantial progress in this direction, with its perplexity almost matching that of a VAE on datasets such as CIFAR-10. In this work, we investigate an alternate training technique for VQ-VAE, inspired by its connection to the Expectation Maximization (EM) algorithm. Training the discrete bottleneck with EM helps us achieve better image generation results on CIFAR-10, and together with knowledge distillation, allows us to develop a non-autoregressive machine translation model whose accuracy almost matches a strong greedy autoregressive baseline Transformer, while being 3.3 times faster at inference.

Includes members:
  Member "deterministic non autoregressive neural sequence modeling by iterative refinement":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "non autoregressive neural dialogue generation")
  Member "discrete autoencoders for sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "model based reinforcement learning for atari")
  Member "fast decoding in sequence models using discrete latent variables":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lava nat a non autoregressive translation model with look around decoding and vocabulary attention")
  Member "backpropagation through the void optimizing control variates for black box gradient estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalizable adversarial attacks with latent variable perturbation modelling")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adaptive density estimation for generative models")
  Member "regularizing neural networks by penalizing confident output distributions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised domain adaptive object detection using forward backward cyclic adaptation")
  Member "parallel multiscale autoregressive density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "videoflow a flow based generative model for video")

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)