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: "corpora generation for grammatical error correction"
  Abstract: Grammatical Error Correction (GEC) has been recently modeled using the sequence-to-sequence framework. However, unlike sequence transduction problems such as machine translation, GEC suffers from the lack of plentiful parallel data. We describe two approaches for generating large parallel datasets for GEC using publicly available Wikipedia data. The first method extracts source-target pairs from Wikipedia edit histories with minimal filtration heuristics, while the second method introduces noise into Wikipedia sentences via round-trip translation through bridge languages. Both strategies yield similar sized parallel corpora containing around 4B tokens. We employ an iterative decoding strategy that is tailored to the loosely supervised nature of our constructed corpora. We demonstrate that neural GEC models trained using either type of corpora give similar performance. Fine-tuning these models on the Lang-8 corpus and ensembling allows us to surpass the state of the art on both the CoNLL-2014 benchmark and the JFLEG task. We provide systematic analysis that compares the two approaches to data generation and highlights the effectiveness of ensembling.

Connected through hyperedge HE-1 (induced by source paper "sequence to sequence pre training with data augmentation for sentence rewriting"):
  with members:
    - "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"
    - "style transfer through back translation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (induced by source paper "parallel iterative edit models for local sequence transduction"):
  with members:
    - "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"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"

Connected through hyperedge HE-4 (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"
    - "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"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"

Connected through hyperedge HE-5 (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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "how to ask better questions a large scale multi domain dataset for rewriting ill formed questions"):
  with members:
    - "translating web search queries into natural language questions"
    - "decomposable neural paraphrase generation"
    - "controllable paraphrase generation with a syntactic exemplar"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "identifying well formed natural language questions"
    - "learning to paraphrase for question answering"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "ask the right questions active question reformulation with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "controllable data synthesis method for grammatical error correction"):
  with members:
    - "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"
    - "understanding back translation at scale"
    - "attention is all you need"
    - "artificial error generation with machine translation and syntactic patterns"
    - "phrase based machine translation is state of the art for automatic grammatical error correction"

Connected through hyperedge HE-8 (induced by source paper "grammatical error correction in low resource scenarios"):
  with members:
    - "revisiting low resource neural machine translation a case study"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "cuni system for the building educational applications 2019 shared task grammatical error correction"
    - "a neural grammatical error correction system built on better pre training and sequential transfer learning"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "attention is all you need"

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)