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: "attention based encoder decoder networks for spelling and grammatical error correction"
  Abstract: Automatic spelling and grammatical correction systems are one of the most widely used tools within natural language applications. In this thesis, we assume the task of error correction as a type of monolingual machine translation where the source sentence is potentially erroneous and the target sentence should be the corrected form of the input. Our main focus in this project is building neural network models for the task of error correction. In particular, we investigate sequence-to-sequence and attention-based models which have recently shown a higher performance than the state-of-the-art of many language processing problems. We demonstrate that neural machine translation models can be successfully applied to the task of error correction. #R##N#While the experiments of this research are performed on an Arabic corpus, our methods in this thesis can be easily applied to any language.

Connected through hyperedge HE-1 (induced by source paper "deep neural network for semantic based text recognition in images"):
  with members:
    - "global encoding for abstractive summarization"
    - "edit probability for scene text recognition"
    - "opennmt open source toolkit for neural machine translation"
    - "neural networks for text correction and completion in keyboard decoding"
    - "protest activity detection and perceived violence estimation from social media images"
    - "cold fusion training seq2seq models together with language models"
    - "text attentional convolutional neural network for scene text detection"
    - "abstractive text summarization using sequence to sequence rnns and beyond"

Connected through hyperedge HE-2 (induced by source paper "deepobfuscode source code obfuscation through sequence to sequence networks"):
  with members:
    - "abstractive and extractive text summarization using document context vector and recurrent neural networks"
    - "cryptodl deep neural networks over encrypted data"
    - "obfuscation using encryption"
    - "assessment of source code obfuscation techniques"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-3 (induced by source paper "utilizing character and word embeddings for text normalization with sequence to sequence models"):
  with members:
    - "deep contextualized word representations"
    - "attention is all you need"
    - "neural language correction with character based attention"
    - "enriching word vectors with subword information"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"
    - "scheduled sampling for sequence prediction with recurrent neural networks"
    - "a primer on neural network models for natural language processing"

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)