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: "approaching neural grammatical error correction as a low resource machine translation task"
  Abstract: Previously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10% M$^2$ on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2% on the CoNLL-2014 benchmark and by 4% on JFLEG.

Connected through hyperedge HE-1 (induced by source paper "pseudo bidirectional decoding for local sequence transduction"):
  with members:
    - "improving grammatical error correction with machine translation pairs"
    - "an empirical study of incorporating pseudo data into grammatical error correction"
    - "sequence to sequence pre training with data augmentation for sentence rewriting"
    - "albert a lite bert for self supervised learning of language representations"
    - "synchronous bidirectional inference for neural sequence generation"
    - "parallel iterative edit models for local sequence transduction"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "denoising based sequence to sequence pre training for text generation"):
  with members:
    - "pre trained language model representations for language generation"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "mass masked sequence to sequence pre training for language generation"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "sql to text generation with graph to sequence model"

Connected through hyperedge HE-3 (induced by source paper "encore ensemble learning using convolution neural machine translation for automatic program repair"):
  with members:
    - "a comprehensive study of automatic program repair on the quixbugs benchmark"
    - "sequencer sequence to sequence learning for end to end program repair"
    - "code2vec learning distributed representations of code"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "fixminer mining relevant fix patterns for automated program repair"
    - "tree2tree neural translation model for learning source code changes"
    - "you cannot fix what you cannot find an investigation of fault localization bias in benchmarking automated program repair systems"

Connected through hyperedge HE-4 (induced by source paper "sequence to sequence pre training with data augmentation for sentence rewriting"):
  with members:
    - "corpora generation for grammatical error correction"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "investigating backtranslation in neural machine translation"
    - "multi task neural models for translating between styles within and across languages"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "style transfer through back translation"

Connected through hyperedge HE-5 (induced by source paper "a neural grammatical error correction system built on better pre training and sequential transfer learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "corpora generation for grammatical error correction"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "weakly supervised grammatical error correction using iterative decoding"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"

Connected through hyperedge HE-6 (induced by source paper "tree transformer a transformer based method for correction of tree structured data"):
  with members:
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "automatic software repair a bibliography"
    - "tree2tree neural translation model for learning source code changes"
    - "learning to repair software vulnerabilities with generative adversarial networks"
    - "tree to tree neural networks for program translation"
    - "semantic code repair using neuro symbolic transformation networks"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "automatically neutralizing subjective bias in text"):
  with members:
    - "towards controlled transformation of sentiment in sentences"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "evaluating style transfer for text"
    - "identifying and reducing gender bias in word level language models"
    - "understanding undesirable word embedding associations"
    - "wikiatomicedits a multilingual corpus of wikipedia edits for modeling language and discourse"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-8 (induced by source paper "parallel iterative edit models for local sequence transduction"):
  with members:
    - "corpora generation for grammatical error correction"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "neural grammatical error correction with finite state transducers"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "learning to represent edits"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"

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)