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: "non autoregressive neural machine translation with enhanced decoder input"
  Abstract: Non-autoregressive translation (NAT) models, which remove the dependence on previous target tokens from the inputs of the decoder, achieve significantly inference speedup but at the cost of inferior accuracy compared to autoregressive translation (AT) models. Previous work shows that the quality of the inputs of the decoder is important and largely impacts the model accuracy. In this paper, we propose two methods to enhance the decoder inputs so as to improve NAT models. The first one directly leverages a phrase table generated by conventional SMT approaches to translate source tokens to target tokens, which are then fed into the decoder as inputs. The second one transforms source-side word embeddings to target-side word embeddings through sentence-level alignment and word-level adversary learning, and then feeds the transformed word embeddings into the decoder as inputs. Experimental results show our method largely outperforms the NAT baseline~\citep{gu2017non} by $5.11$ BLEU scores on WMT14 English-German task and $4.72$ BLEU scores on WMT16 English-Romanian task.

Connected through hyperedge HE-1 (induced by source paper "pay less attention with lightweight and dynamic convolutions"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "bi directional block self attention for fast and memory efficient sequence modeling"
    - "self attention with relative position representations"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "scaling neural machine translation"
    - "smoothed dilated convolutions for improved dense prediction"
    - "the best of both worlds combining recent advances in neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "multilingual neural machine translation with knowledge distillation"):
  with members:
    - "knowledge distillation by on the fly native ensemble"
    - "sentence wise smooth regularization for sequence to sequence learning"
    - "universal neural machine translation for extremely low resource languages"
    - "meta learning for low resource neural machine translation"
    - "large scale distributed neural network training through online distillation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "double path networks for sequence to sequence learning"
    - "knowledge distillation in generations more tolerant teachers educate better students"

Connected through hyperedge HE-3 (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"
    - "mask predict parallel decoding of conditional masked language models"
    - "memory attended recurrent network for video captioning"
    - "a neural compositional paradigm for image captioning"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "reconstruction network for video captioning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "hard but robust easy but sensitive how encoder and decoder perform in neural machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "hybrid self attention network for machine translation"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "dense information flow for neural machine translation"

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"
    - "fast decoding in sequence models using discrete latent variables"
    - "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"
    - "fast decoding in sequence models using discrete latent variables"
    - "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 "fine tuning by curriculum learning for non autoregressive neural machine translation"):
  with members:
    - "non autoregressive machine translation with auxiliary regularization"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "hint based training for non autoregressive machine translation"
    - "on the power of curriculum learning in training deep networks"
    - "fastspeech fast robust and controllable text to speech"
    - "mass masked sequence to sequence pre training for language generation"
    - "non monotonic sequential text generation"
    - "achieving human parity on automatic chinese to english news translation"

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)