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: "sentence level grammatical error identification as sequence to sequence correction"
  Abstract: We demonstrate that an attention-based encoder-decoder model can be used for sentence-level grammatical error identification for the Automated Evaluation of Scientific Writing (AESW) Shared Task 2016. The attention-based encoder-decoder models can be used for the generation of corrections, in addition to error identification, which is of interest for certain end-user applications. We show that a character-based encoder-decoder model is particularly effective, outperforming other results on the AESW Shared Task on its own, and showing gains over a word-based counterpart. Our final model--a combination of three character-based encoder-decoder models, one word-based encoder-decoder model, and a sentence-level CNN--is the highest performing system on the AESW 2016 binary prediction Shared Task.

Connected through hyperedge HE-1 (induced by source paper "combating adversarial misspellings with robust word recognition"):
  with members:
    - "on adversarial examples for character level neural machine translation"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "spelling error correction using a nested rnn model and pseudo training data"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "synthetic and natural noise both break neural machine translation"
    - "adversarial examples for evaluating reading comprehension systems"
    - "generating natural adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "identifying well formed natural language questions"):
  with members:
    - "learning to ask neural question generation for reading comprehension"
    - "ask the right questions active question reformulation with reinforcement learning"
    - "bilateral multi perspective matching for natural language sentences"
    - "squad 100 000 questions for machine comprehension of text"
    - "generating natural questions about an image"
    - "microsoft coco captions data collection and evaluation server"
    - "a large annotated corpus for learning natural language inference"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-3 (induced by source paper "sentence level fluency evaluation references help but can be spared"):
  with members:
    - "fluency over adequacy a pilot study in measuring user trust in imperfect mt"
    - "a challenge set approach to evaluating machine translation"
    - "referenceless quality estimation for natural language generation"
    - "exploiting unlabeled data for neural grammatical error detection"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "a nested attention neural hybrid model for grammatical error correction"):
  with members:
    - "neural network translation models for grammatical error 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-5 (induced by source paper "semantic code repair using neuro symbolic transformation networks"):
  with members:
    - "learning python code suggestion with a sparse pointer network"
    - "cuni system for wmt16 automatic post editing and multimodal translation tasks"
    - "automated correction for syntax errors in programming assignments using recurrent neural networks"
    - "learning to superoptimize programs"
    - "a convolutional attention network for extreme summarization of source code"
    - "neural module networks"
    - "neural machine translation by jointly learning to align and translate"
    - "automated feedback generation for introductory programming assignments"

Connected through hyperedge HE-6 (induced by source paper "still not there comparing traditional sequence to sequence models to encoder decoder neural networks on monotone string translation tasks"):
  with members:
    - "neural language correction with character based attention"
    - "attention based convolutional neural network for machine comprehension"
    - "effective approaches to attention based neural machine translation"
    - "sequence to sequence neural net models for grapheme to phoneme conversion"
    - "a neural conversational model"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-7 (induced by source paper "exploiting unlabeled data for neural grammatical error detection"):
  with members:
    - "end to end memory networks"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "robsut wrod reocginiton via semi character recurrent neural network"):
  with members:
    - "character based neural machine translation"
    - "improved transition based parsing by modeling characters instead of words with lstms"
    - "character aware neural language models"
    - "text segmentation with character level text embeddings"

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)