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: "quickedit editing text translations by crossing words out"
  Abstract: We propose a framework for computer-assisted text editing. It applies to translation post-editing and to paraphrasing. Our proposal relies on very simple interactions: a human editor modifies a sentence by marking tokens they would like the system to change. Our model then generates a new sentence which reformulates the initial sentence by avoiding marked words. The approach builds upon neural sequence-to-sequence modeling and introduces a neural network which takes as input a sentence along with change markers. Our model is trained on translation bitext by simulating post-edits. We demonstrate the advantage of our approach for translation post-editing through simulated post-edits. We also evaluate our model for paraphrasing through a user study.

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 "improving multi turn dialogue modelling with utterance rewriter"):
  with members:
    - "review driven answer generation for product related questions in e commerce"
    - "sentence rewriting for semantic parsing"
    - "cota improving the speed and accuracy of customer support through ranking and deep networks"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "fast abstractive summarization with reinforce selected sentence rewriting"
    - "an exploration of neural sequence to sequence architectures for automatic post editing"
    - "end to end neural coreference resolution"
    - "a knowledge grounded neural conversation model"

Connected through hyperedge HE-3 (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"
    - "theory and experiments on vector quantized autoencoders"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "diamonds in the rough generating fluent sentences from early stage drafts for academic writing assistance"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "corpora generation for grammatical error correction"
    - "bertscore evaluating text generation with bert"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "text infilling"
    - "style transfer through back translation"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "content preserving text generation with attribute controls"

Connected through hyperedge HE-5 (induced by source paper "neural machine translation with noisy lexical constraints"):
  with members:
    - "non parametric adaptation for neural machine translation"
    - "training neural machine translation to apply terminology constraints"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "neural machine translation decoding with terminology constraints"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "lexically constrained decoding for sequence generation using grid beam search"

Connected through hyperedge HE-6 (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-7 (induced by source paper "controllable sentence simplification employing syntactic and lexical constraints"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "sentence simplification with memory augmented neural networks"
    - "attention is all you need"
    - "sentence simplification with deep reinforcement learning"
    - "controlling linguistic style aspects in neural language generation"
    - "get to the point summarization with pointer generator networks"
    - "controllable abstractive summarization"

Connected through hyperedge HE-8 (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"
    - "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"
    - "iterative refinement for machine 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)