human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sequence to sequence pre training with data augmentation for sentence rewriting"):
  Abstract: We study sequence-to-sequence (seq2seq) pre-training with data augmentation for sentence rewriting. Instead of training a seq2seq model with gold training data and augmented data simultaneously, we separate them to train in different phases: pre-training with the augmented data and fine-tuning with the gold data. We also introduce multiple data augmentation methods to help model pre-training for sentence rewriting. We evaluate our approach in two typical well-defined sentence rewriting tasks: Grammatical Error Correction (GEC) and Formality Style Transfer (FST). Experiments demonstrate our approach can better utilize augmented data without hurting the model's trust in gold data and further improve the model's performance with our proposed data augmentation methods. #R##N#Our approach substantially advances the state-of-the-art results in well-recognized sentence rewriting benchmarks over both GEC and FST. Specifically, it pushes the CoNLL-2014 benchmark's $F_{0.5}$ score and JFLEG Test GLEU score to 62.61 and 63.54 in the restricted training setting, 66.77 and 65.22 respectively in the unrestricted setting, and advances GYAFC benchmark's BLEU to 74.24 (2.23 absolute improvement) in E&M domain and 77.97 (2.64 absolute improvement) in F&R domain.

Includes members:
  Member "corpora generation for grammatical error correction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a neural grammatical error correction system built on better pre training and sequential transfer learning")
  Member "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue")
  Member "investigating backtranslation in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "do all roads lead to rome understanding the role of initialization in iterative back translation")
  Member "multi task neural models for translating between styles within and across languages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "controlling the output length of neural machine translation")
  Member "approaching neural grammatical error correction as a low resource machine translation task":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pseudo bidirectional decoding for local sequence transduction")
  Member "near human level performance in grammatical error correction with hybrid machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "denoising based sequence to sequence pre training for text generation")
  Member "reaching human level performance in automatic grammatical error correction an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "denoising based sequence to sequence pre training for text generation")
  Member "a multilayer convolutional encoder decoder neural network for grammatical error correction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "denoising based sequence to sequence pre training for text generation")

Question: Please tell me which class the source paper 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)