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: "a nested attention neural hybrid model for grammatical error correction"
  Abstract: Grammatical error correction (GEC) systems strive to correct both global errors in word order and usage, and local errors in spelling and inflection. Further developing upon recent work on neural machine translation, we propose a new hybrid neural model with nested attention layers for GEC. Experiments show that the new model can effectively correct errors of both types by incorporating word and character-level information,and that the model significantly outperforms previous neural models for GEC as measured on the standard CoNLL-14 benchmark dataset. Further analysis also shows that the superiority of the proposed model can be largely attributed to the use of the nested attention mechanism, which has proven particularly effective in correcting local errors that involve small edits in orthography.

Connected through hyperedge HE-1 (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"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "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-2 (induced by source paper "tree transformer a transformer based method for correction of tree structured data"):
  with members:
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "adapting sequence to sequence models for text normalization in social media"):
  with members:
    - "learning to compute word embeddings on the fly"
    - "improving text normalization by optimizing nearest neighbor matching"
    - "get to the point summarization with pointer generator networks"
    - "bpemb tokenization free pre trained subword embeddings in 275 languages"
    - "monoise modeling noise using a modular normalization system"
    - "synthetic and natural noise both break neural machine translation"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "achieving open vocabulary neural machine translation with hybrid word character models"

Connected through hyperedge HE-4 (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"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "an empirical study of incorporating pseudo data into grammatical error correction"):
  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"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "understanding back translation at scale"

Connected through hyperedge HE-6 (induced by source paper "towards content transfer through grounded text generation"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "style transfer through back translation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical neural story generation"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "style transfer from non parallel text by cross alignment"
    - "controlling linguistic style aspects in neural language generation"

Connected through hyperedge HE-7 (induced by source paper "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"):
  with members:
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "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"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "learning multi level dependencies for robust word recognition"):
  with members:
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "combating adversarial misspellings with robust word recognition"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "build it break it fix it for dialogue safety robustness from adversarial human attack"
    - "say what i want towards the dark side of neural dialogue models"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "adversarial example generation with syntactically controlled paraphrase networks"

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)