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 networks for text correction and completion in keyboard decoding"
  Abstract: Despite the ubiquity of mobile and wearable text messaging applications, the problem of keyboard text decoding is not tackled sufficiently in the light of the enormous success of the deep learning Recurrent Neural Network (RNN) and Convolutional Neural Networks (CNN) for natural language understanding. In particular, considering that the keyboard decoders should operate on devices with memory and processor resource constraints, makes it challenging to deploy industrial scale deep neural network (DNN) models. This paper proposes a sequence-to-sequence neural attention network system for automatic text correction and completion. Given an erroneous sequence, our model encodes character level hidden representations and then decodes the revised sequence thus enabling auto-correction and completion. We achieve this by a combination of character level CNN and gated recurrent unit (GRU) encoder along with and a word level gated recurrent unit (GRU) attention decoder. Unlike traditional language models that learn from billions of words, our corpus size is only 12 million words; an order of magnitude smaller. The memory footprint of our learnt model for inference and prediction is also an order of magnitude smaller than the conventional language model based text decoders. We report baseline performance for neural keyboard decoders in such limited domain. Our models achieve a word level accuracy of $90\%$ and a character error rate CER of $2.4\%$ over the Twitter typo dataset. We present a novel dataset of noisy to corrected mappings by inducing the noise distribution from the Twitter data over the OpenSubtitles 2009 dataset; on which our model predicts with a word level accuracy of $98\%$ and sequence accuracy of $68.9\%$. In our user study, our model achieved an average CER of $2.6\%$ with the state-of-the-art non-neural touch-screen keyboard decoder at CER of $1.6\%$.

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"
    - "attention based encoder decoder networks for spelling and grammatical error correction"
    - "edit probability for scene text recognition"
    - "opennmt open source toolkit for neural machine translation"
    - "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 "a computationally efficient pipeline approach to full page offline handwritten text recognition"):
  with members:
    - "accurate data efficient unconstrained text recognition with convolutional neural networks"
    - "attention is all you need"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "ssd single shot multibox detector"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-3 (induced by source paper "context sensitive malicious spelling error correction"):
  with members:
    - "deep learning for detecting cyberbullying across multiple social media platforms"
    - "impact of content features for automatic online abuse detection"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "geometry of compositionality"
    - "polyglot distributed word representations for multilingual nlp"
    - "distributed representations of words and phrases and their compositionality"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-4 (induced by source paper "federated learning for mobile keyboard prediction"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "lstm a search space odyssey"
    - "mobile keyboard input decoding with finite state transducers"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "practical secure aggregation for federated learning on user held data"
    - "communication efficient learning of deep networks from decentralized data"
    - "using the output embedding to improve language models"
    - "exploring the limits of language modeling"

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)