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: "fast decoding in sequence models using discrete latent variables"
  Abstract: Autoregressive sequence models based on deep neural networks, such as RNNs, Wavenet and the Transformer attain state-of-the-art results on many tasks. However, they are difficult to parallelize and are thus slow at processing long sequences. RNNs lack parallelism both during training and decoding, while architectures like WaveNet and Transformer are much more parallelizable during training, yet still operate sequentially during decoding. #R##N#Inspired by [arxiv:1711.00937], we present a method to extend sequence models using discrete latent variables that makes decoding much more parallelizable. We first auto-encode the target sequence into a shorter sequence of discrete latent variables, which at inference time is generated autoregressively, and finally decode the output sequence from this shorter latent sequence in parallel. To this end, we introduce a novel method for constructing a sequence of discrete latent variables and compare it with previously introduced methods. Finally, we evaluate our model end-to-end on the task of neural machine translation, where it is an order of magnitude faster at decoding than comparable autoregressive models. While lower in BLEU than purely autoregressive models, our model achieves higher scores than previously proposed non-autogregressive translation models.

Connected through hyperedge HE-1 (induced by source paper "lava nat a non autoregressive translation model with look around decoding and vocabulary attention"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "hint based training for non autoregressive machine translation"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "fast structured decoding for sequence models"
    - "pnat non autoregressive transformer by position learning"
    - "levenshtein transformer"
    - "flowseq non autoregressive conditional sequence generation with generative flow"
    - "self attention with relative position representations"

Connected through hyperedge HE-2 (induced by source paper "information maximizing visual question generation"):
  with members:
    - "neural baby talk"
    - "visual curiosity learning to ask questions to learn visual recognition"
    - "learning to ask neural question generation for reading comprehension"
    - "inferring and executing programs for visual reasoning"
    - "visual question generation as dual task of visual question answering"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "a simple neural network module for relational reasoning"
    - "learning to reason end to end module networks for visual question answering"

Connected through hyperedge HE-3 (induced by source paper "real or fake learning to discriminate machine from human generated text"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "implicit generation and generalization in energy based models"
    - "gltr statistical detection and visualization of generated text"
    - "defending against neural fake news"
    - "roberta a robustly optimized bert pretraining approach"
    - "importance of search and evaluation strategies in neural dialogue modeling"

Connected through hyperedge HE-4 (induced by source paper "mixture models for diverse machine translation tricks of the trade"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "achieving human parity on automatic chinese to english news translation"
    - "scaling neural machine translation"
    - "hierarchical neural story generation"
    - "sequence to sequence mixture model for diverse machine translation"
    - "understanding back translation at scale"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "latent intention dialogue models"

Connected through hyperedge HE-5 (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-6 (induced by source paper "retrieving sequential information for non autoregressive neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "you may not need attention"
    - "a study of reinforcement learning for neural machine translation"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "bag of words as target for neural machine translation"
    - "speeding up neural machine translation decoding by cube pruning"
    - "accelerating neural transformer via an average attention network"

Connected through hyperedge HE-7 (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"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "theory and experiments on vector quantized autoencoders"
    - "end to end non autoregressive neural machine translation with connectionist temporal classification"

Connected through hyperedge HE-8 (induced by source paper "towards interlingua neural machine translation"):
  with members:
    - "the missing ingredient in zero shot neural machine translation"
    - "unsupervised statistical machine translation"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "a neural interlingua for multilingual machine translation"
    - "phrase based neural unsupervised machine translation"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"
    - "unsupervised machine translation using monolingual corpora only"

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)