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: "iterative refinement for machine translation"
  Abstract: Existing machine translation decoding algorithms generate translations in a strictly monotonic fashion and never revisit previous decisions. As a result, earlier mistakes cannot be corrected at a later stage. In this paper, we present a translation scheme that starts from an initial guess and then makes iterative improvements that may revisit previous decisions. We parameterize our model as a convolutional neural network that predicts discrete substitutions to an existing translation based on an attention mechanism over both the source sentence as well as the current translation output. By making less than one modification per sentence, we improve the output of a phrase-based translation system by up to 0.4 BLEU on WMT15 German-English translation.

Connected through hyperedge HE-1 (induced by source paper "xl editor post editing sentences with xlnet"):
  with members:
    - "insertion transformer flexible sequence generation via insertion operations"
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "a dual reinforcement learning framework for unsupervised text style transfer"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "text repair model for neural machine translation"
    - "levenshtein transformer"
    - "text infilling"
    - "kermit generative insertion based modeling for sequences"

Connected through hyperedge HE-2 (induced by source paper "semantic role labeling with iterative structure refinement"):
  with members:
    - "dependency or span end to end uniform semantic role labeling"
    - "second order semantic dependency parsing with end to end neural networks"
    - "capturing argument interaction in semantic role labeling with capsule networks"
    - "benchmarking approximate inference methods for neural structured prediction"
    - "achieving human parity on automatic chinese to english news translation"
    - "polyglot semantic role labeling"
    - "image segmentation by iterative inference from conditional score estimation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"

Connected through hyperedge HE-3 (induced by source paper "distilling translations with visual awareness"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "probing the need for visual context in multimodal machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "getting gender right in neural machine translation"
    - "object counts bringing explicit detections back into image captioning"
    - "cuni system for the wmt18 multimodal translation task"

Connected through hyperedge HE-4 (induced by source paper "levenshtein transformer"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "editnts an neural programmer interpreter model for sentence simplification through explicit editing"
    - "text repair model for neural machine translation"
    - "non monotonic sequential text generation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "blockwise parallel decoding for deep autoregressive models"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "deterministic non autoregressive neural sequence modeling by iterative refinement"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "unsupervised machine translation using monolingual corpora only"
    - "quickedit editing text translations by crossing words out"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "state of the art speech recognition with sequence to sequence models"
    - "non autoregressive neural machine translation"
    - "unsupervised neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "neural networks for modeling source code edits"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "progressive growing of gans for improved quality stability and variation"
    - "attention is all you need"
    - "adapting sequence models for sentence correction"
    - "a survey of machine learning for big code and naturalness"
    - "enhanced neural machine translation by learning from draft"
    - "learning python code suggestion with a sparse pointer network"
    - "pre translation for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "retrieve and refine improved sequence generation models for dialogue"):
  with members:
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "generating sentences by editing prototypes"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "quickedit editing text translations by crossing words out"
    - "adapting sequence models for sentence correction"
    - "controllable abstractive summarization"
    - "search engine guided non parametric neural machine translation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-8 (induced by source paper "enhanced neural machine translation by learning from draft"):
  with members:
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "improving neural machine translation models with monolingual data"
    - "effective approaches to attention based neural machine translation"
    - "draw a recurrent neural network for image generation"
    - "adam a method for stochastic optimization"
    - "on the properties of neural machine translation encoder decoder approaches"

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)