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: "mask predict parallel decoding of conditional masked language models"
  Abstract: Most machine translation systems generate text autoregressively from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves state-of-the-art performance levels for non-autoregressive and parallel decoding translation models by over 4 BLEU on average. It is also able to reach within about 1 BLEU point of a typical left-to-right transformer model, while decoding significantly faster.

Connected through hyperedge HE-1 (induced by source paper "blank language models"):
  with members:
    - "tigs an inference algorithm for text infilling with gradient search"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "synchronous bidirectional neural machine translation"
    - "restoring ancient text using deep learning a case study on greek epigraphy"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "non monotonic sequential text generation"

Connected through hyperedge HE-2 (induced by source paper "parallel machine translation with disentangled context transformer"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "cloze driven pretraining of self attention networks"
    - "fast structured decoding for sequence models"
    - "levenshtein transformer"
    - "flowseq non autoregressive conditional sequence generation with generative flow"

Connected through hyperedge HE-3 (induced by source paper "semi autoregressive training improves mask predict decoding"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "pay less attention with lightweight and dynamic convolutions"
    - "understanding knowledge distillation in non autoregressive machine translation"
    - "fast structured decoding for sequence models"
    - "levenshtein transformer"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"

Connected through hyperedge HE-4 (induced by source paper "distilling the knowledge of bert for text generation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "towards making the most of bert in neural machine translation"
    - "multilingual neural machine translation with knowledge distillation"
    - "mass masked sequence to sequence pre training for language generation"
    - "pretraining based natural language generation for text summarization"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "patient knowledge distillation for bert model compression"
    - "improving sequence to sequence learning via optimal transport"

Connected through hyperedge HE-5 (induced by source paper "non autoregressive video captioning with iterative refinement"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "memory attended recurrent network for video captioning"
    - "a neural compositional paradigm for image captioning"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reconstruction network for video captioning"

Connected through hyperedge HE-6 (induced by source paper "defending against neural fake news"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "release strategies and the social impacts of language models"
    - "unsupervised domain adaptation of contextualized embeddings for sequence labeling"
    - "from recognition to cognition visual commonsense reasoning"

Connected through hyperedge HE-7 (induced by source paper "understanding knowledge distillation in non autoregressive machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "syntactically supervised transformers for faster neural machine translation"
    - "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior"
    - "retrieving sequential information for non autoregressive neural machine translation"
    - "imitation learning for non autoregressive neural machine translation"
    - "mixture models for diverse machine translation tricks of the trade"

Connected through hyperedge HE-8 (induced by source paper "how can we know what language models know"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "matching the blanks distributional similarity for relation learning"
    - "latent relation language models"
    - "knowledge enhanced contextual word representations"

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.CL(Computation and Language)