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 "neural grammatical error correction with finite state transducers"):
  Abstract: Grammatical error correction (GEC) is one of the areas in natural language processing in which purely neural models have not yet superseded more traditional symbolic models. Hybrid systems combining phrase-based statistical machine translation (SMT) and neural sequence models are currently among the most effective approaches to GEC. However, both SMT and neural sequence-to-sequence models require large amounts of annotated data. Language model based GEC (LM-GEC) is a promising alternative which does not rely on annotated training data. We show how to improve LM-GEC by applying modelling techniques based on finite state transducers. We report further gains by rescoring with neural language models. We show that our methods developed for LM-GEC can also be used with SMT systems if annotated training data is available. Our best system outperforms the best published result on the CoNLL-2014 test set, and achieves far better relative improvements over the SMT baselines than previous hybrid systems.

Includes members:
  Member "near human level performance in grammatical error correction with hybrid machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "denoising based sequence to sequence pre training for text generation")
  Member "jfleg a fluency corpus and benchmark for grammatical error correction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "denoising based sequence to sequence pre training for text generation")
  Member "a nested attention neural hybrid model for grammatical error correction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sequence to sequence pre training with data augmentation for sentence rewriting")
  Member "adapting sequence models for sentence correction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploring and improving robustness of multi task deep neural networks via domain agnostic defenses")
  Member "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cued wmt19 ewc lms")
  Member "grammatical error correction with neural reinforcement learning":
    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)