human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "minimizing the bag of ngrams difference for non autoregressive neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "syntactically supervised transformers for faster neural machine translation"
    - "imitation learning for non autoregressive neural machine translation"
    - "bridging the gap between training and inference for neural machine translation"
    - "pnat non autoregressive transformer by position learning"
    - "levenshtein transformer"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "guiding non autoregressive neural machine translation decoding with reordering information"

Connected through hyperedge HE-2 (induced by source paper "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "preventing posterior collapse with delta vaes"
    - "non monotonic sequential text generation"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "image transformer"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-3 (induced by source paper "fast structured decoding for sequence models"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "hint based training for non autoregressive machine translation"
    - "tensor2tensor for neural machine translation"
    - "a tutorial on deep latent variable models of natural language"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "fast decoding in sequence models using discrete latent variables"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "end to end non autoregressive neural machine translation with connectionist temporal classification"

Connected through hyperedge HE-4 (induced by source paper "unsupervised paraphrasing without translation"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "fast decoding in sequence models using discrete latent variables"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "phrase based neural unsupervised machine translation"
    - "quickedit editing text translations by crossing words out"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "preventing posterior collapse with delta vaes"):
  with members:
    - "uncertainty in the variational information bottleneck"
    - "stochastic video generation with a learned prior"
    - "autoregressive quantile networks for generative modeling"
    - "spherical latent spaces for stable variational autoencoders"
    - "associative compression networks for representation learning"
    - "generating sentences by editing prototypes"
    - "attention is all you need"
    - "stochastic variational video prediction"

Connected through hyperedge HE-6 (induced by source paper "supervised vector quantized variational autoencoder for learning interpretable global representations"):
  with members:
    - "fast decoding in sequence models using discrete latent variables"
    - "neural discrete representation learning"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

Connected through hyperedge HE-7 (induced by source paper "extractive summary as discrete latent variables"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "achieving human parity on automatic chinese to english news translation"
    - "on accurate evaluation of gans for language generation"
    - "generating wikipedia by summarizing long sequences"
    - "hierarchical text generation using an outline"
    - "discrete autoencoders for sequence models"
    - "deep contextualized word representations"

Connected through hyperedge HE-8 (induced by source paper "neural joint source channel coding"):
  with members:
    - "deepcode feedback codes via deep learning"
    - "deep joint source channel coding for wireless image transmission"
    - "discrete autoencoders for sequence models"
    - "deep learning for joint source channel coding of text"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "infovae information maximizing variational autoencoders"
    - "scaling deep learning based decoding of polar codes via partitioning"

Question: Please tell me which class the center node 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)