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: "what you get is what you see a visual markup decompiler"
  Abstract: Building on recent advances in image caption generation and optical character recognition (OCR), we present a general-purpose, deep learning-based system to decompile an image into presentational markup. While this task is a well-studied problem in OCR, our method takes an inherently different, data-driven approach. Our model does not require any knowledge of the underlying markup language, and is simply trained end-to-end on real-world example data. The model employs a convolutional network for text and layout recognition in tandem with an attention-based neural machine translation system. To train and evaluate the model, we introduce a new dataset of real-world rendered mathematical expressions paired with LaTeX markup, as well as a synthetic dataset of web pages paired with HTML snippets. Experimental results show that the system is surprisingly effective at generating accurate markup for both datasets. While a standard domain-specific LaTeX OCR system achieves around 25% accuracy, our model reproduces the exact rendered image on 75% of examples.

Connected through hyperedge HE-1 (induced by source paper "scene text recognition via transformer"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "icdar2019 robust reading challenge on arbitrary shaped text rrc art"
    - "2d attentional irregular scene text recognizer"
    - "a simple and robust convolutional attention network for irregular text recognition"
    - "scene text recognition from two dimensional perspective"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "edit probability for scene text recognition"
    - "show attend and read a simple and strong baseline for irregular text recognition"

Connected through hyperedge HE-2 (induced by source paper "scanssd scanning single shot detector for mathematical formulas in pdf document images"):
  with members:
    - "focal loss for dense object detection"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "textboxes a fast text detector with a single deep neural network"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "translating mathematical formula images to latex sequences using deep neural networks with sequence level training"):
  with members:
    - "a study of reinforcement learning for neural machine translation"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "attention is all you need"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "effective approaches to attention based neural machine translation"
    - "minimum risk training for neural machine translation"
    - "sequence level training with recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "tablebank table benchmark for image based table detection and recognition"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "imagenet large scale visual recognition challenge"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "multi scale attention with dense encoder for handwritten mathematical expression recognition"):
  with members:
    - "trajectory based radical analysis network for online handwritten chinese character recognition"
    - "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"
    - "radical analysis network for zero shot learning in printed chinese character recognition"
    - "densely connected convolutional networks"
    - "modeling coverage for neural machine translation"
    - "end to end attention based large vocabulary speech recognition"
    - "show attend and tell neural image caption generation with visual attention"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "learning character level compositionality with visual features"):
  with members:
    - "tweet2vec character based distributed representations for social media"
    - "achieving open vocabulary neural machine translation with hybrid word character models"
    - "visual7w grounded question answering in images"
    - "multilingual language processing from bytes"
    - "finding function in form compositional character models for open vocabulary word representation"
    - "skip thought vectors"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-7 (induced by source paper "opennmt open source toolkit for neural machine translation"):
  with members:
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "sequence to sequence rnns for text summarization"
    - "linguistic input features improve neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "rnn recurrent library for torch"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-8 (induced by source paper "a gru based encoder decoder approach with attention for online handwritten mathematical expression recognition"):
  with members:
    - "modeling coverage for neural machine translation"
    - "drawing and recognizing chinese characters with recurrent neural network"
    - "end to end attention based large vocabulary speech recognition"
    - "natural language understanding with distributed representation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "end to end continuous speech recognition using attention based recurrent nn first results"

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.CV(Computer Vision and Pattern Recognition)