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: "visual attention models for scene text recognition"
  Abstract: In this paper we propose an approach to lexicon-free recognition of text in scene images. Our approach relies on a LSTM-based soft visual attention model learned from convolutional features. A set of feature vectors are derived from an intermediate convolutional layer corresponding to different areas of the image. This permits encoding of spatial information into the image representation. In this way, the framework is able to learn how to selectively focus on different parts of the image. At every time step the recognizer emits one character using a weighted combination of the convolutional feature vectors according to the learned attention model. Training can be done end-to-end using only word level annotations. In addition, we show that modifying the beam search algorithm by integrating an explicit language model leads to significantly better recognition results. We validate the performance of our approach on standard SVT and ICDAR'03 scene text datasets, showing state-of-the-art performance in unconstrained text recognition.

Connected through hyperedge HE-1 (induced by source paper "towards unconstrained end to end text spotting"):
  with members:
    - "textfield learning a deep direction field for irregular scene text detection"
    - "msr multi scale shape regression for scene text detection"
    - "rotation sensitive regression for oriented scene text detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "e2e mlt an unconstrained end to end method for multi language scene text"

Connected through hyperedge HE-2 (induced by source paper "2d ctc for scene text recognition"):
  with members:
    - "scene text recognition from two dimensional perspective"
    - "an end to end textspotter with explicit alignment and attention"
    - "edit probability for scene text recognition"
    - "scan sliding convolutional attention network for scene text recognition"
    - "esir end to end scene text recognition via iterative image rectification"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "aon towards arbitrarily oriented text recognition"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-3 (induced by source paper "textscanner reading characters in order for robust scene text recognition"):
  with members:
    - "scene text recognition from two dimensional perspective"
    - "edit probability for scene text recognition"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "scene text detection and recognition the deep learning era"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "aon towards arbitrarily oriented text recognition"
    - "icdar2017 competition on reading chinese text in the wild rctw 17"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-4 (induced by source paper "semantic relatedness based re ranker for text spotting"):
  with members:
    - "universal sentence encoder"
    - "visual re ranking with natural language understanding for text spotting"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "multichannel variable size convolution for sentence classification"
    - "a decomposable attention model for natural language inference"
    - "breakingnews article annotation by image and text processing"
    - "bag of tricks for efficient text classification"
    - "coco text dataset and benchmark for text detection and recognition in natural images"

Connected through hyperedge HE-5 (induced by source paper "nrtr a no recurrence sequence to sequence model for scene text recognition"):
  with members:
    - "scene text recognition from two dimensional perspective"
    - "edit probability for scene text recognition"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "attention is all you need"
    - "aon towards arbitrarily oriented text recognition"
    - "reading scene text with attention convolutional sequence modeling"
    - "scene text recognition with sliding convolutional character models"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-6 (induced by source paper "visual re ranking with natural language understanding for text spotting"):
  with members:
    - "reading scene text with attention convolutional sequence modeling"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "enriching word vectors with subword information"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "synthetic data and artificial neural networks for natural scene text recognition"

Connected through hyperedge HE-7 (induced by source paper "visual semantic re ranker for text spotting"):
  with members:
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "reading text in the wild with convolutional neural networks"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "confidence prediction for lexicon free ocr"):
  with members:
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "synthetic data and artificial neural networks for natural scene text recognition"

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)