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: "neural network translation models for grammatical error correction"
  Abstract: Phrase-based statistical machine translation (SMT) systems have previously been used for the task of grammatical error correction (GEC) to achieve state-of-the-art accuracy. The superiority of SMT systems comes from their ability to learn text transformations from erroneous to corrected text, without explicitly modeling error types. However, phrase-based SMT systems suffer from limitations of discrete word representation, linear mapping, and lack of global context. In this paper, we address these limitations by using two different yet complementary neural network models, namely a neural network global lexicon model and a neural network joint model. These neural networks can generalize better by using continuous space representation of words and learn non-linear mappings. Moreover, they can leverage contextual information from the source sentence more effectively. By adding these two components, we achieve statistically significant improvement in accuracy for grammatical error correction over a state-of-the-art GEC system.

Connected through hyperedge HE-1 (induced by source paper "the unreasonable effectiveness of transformer language models in grammatical error correction"):
  with members:
    - "assessing bert s syntactic abilities"
    - "parameter efficient transfer learning for nlp"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "reaching human level performance in automatic grammatical error correction an empirical study"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-2 (induced by source paper "neural grammatical error correction with finite state transducers"):
  with members:
    - "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"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "adapting sequence models for sentence correction"
    - "sgnmt a flexible nmt decoding platform for quick prototyping of new models and search strategies"
    - "grammatical error correction with neural reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "reaching human level performance in automatic grammatical error correction an empirical study"):
  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"
    - "a multilayer convolutional encoder decoder neural network for grammatical error correction"
    - "inherent biases in reference based evaluation for grammatical error correction and text simplification"
    - "joint training for neural machine translation models with monolingual data"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"
    - "attention is all you need"
    - "adapting sequence models for sentence correction"

Connected through hyperedge HE-4 (induced by source paper "a multilayer convolutional encoder decoder neural network for grammatical error correction"):
  with members:
    - "a nested attention neural hybrid model for grammatical error correction"
    - "adapting sequence models for sentence correction"
    - "convolutional sequence to sequence learning"
    - "neural language correction with character based attention"
    - "language modeling with gated convolutional networks"
    - "enriching word vectors with subword information"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-5 (induced by source paper "a nested attention neural hybrid model for grammatical error correction"):
  with members:
    - "sentence level grammatical error identification as sequence to sequence correction"
    - "phrase based machine translation is state of the art for automatic grammatical error correction"
    - "neural language correction with character based attention"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "fully character level neural machine translation without explicit segmentation"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-6 (induced by source paper "phrase based machine translation is state of the art for automatic grammatical error correction"):
  with members:
    - "neural language correction with character based attention"
    - "exploiting n best hypotheses to improve an smt approach to grammatical error correction"
    - "efficient estimation of word representations in vector space"

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)