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: "show attend and read a simple and strong baseline for irregular text recognition"
  Abstract: Recognizing irregular text in natural scene images is challenging due to the large variance in text appearance, such as curvature, orientation and distortion. Most existing approaches rely heavily on sophisticated model designs and/or extra fine-grained annotations, which, to some extent, increase the difficulty in algorithm implementation and data collection. In this work, we propose an easy-to-implement strong baseline for irregular scene text recognition, using off-the-shelf neural network components and only word-level annotations. It is composed of a $31$-layer ResNet, an LSTM-based encoder-decoder framework and a 2-dimensional attention module. Despite its simplicity, the proposed method is robust and achieves state-of-the-art performance on both regular and irregular scene text recognition benchmarks. Code is available at: this https URL

Connected through hyperedge HE-1 (induced by source paper "efficient backbone search for scene text recognition"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "detnas backbone search for object detection"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (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"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"

Connected through hyperedge HE-3 (induced by source paper "separating content from style using adversarial learning for recognizing text in the wild"):
  with members:
    - "controllable artistic text style transfer via shape matching gan"
    - "scene text recognition from two dimensional perspective"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "began boundary equilibrium generative adversarial networks"
    - "neural style transfer a review"
    - "multi content gan for few shot font style transfer"
    - "focusing attention towards accurate text recognition in natural images"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "faclstm convlstm with focused attention for scene text recognition"):
  with members:
    - "scene text recognition from two dimensional perspective"
    - "edit probability for scene text recognition"
    - "stn ocr a single neural network for text detection and text recognition"
    - "aon towards arbitrarily oriented text recognition"
    - "attention based extraction of structured information from street view imagery"
    - "reading scene text with attention convolutional sequence modeling"
    - "deformable convolutional networks"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-5 (induced by source paper "learn to augment joint data augmentation and network optimization for text recognition"):
  with members:
    - "symmetry constrained rectification network for scene text recognition"
    - "scene text recognition from two dimensional perspective"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "edit probability for scene text recognition"
    - "esir end to end scene text recognition via iterative image rectification"
    - "aon towards arbitrarily oriented text recognition"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-6 (induced by source paper "gtc guided training of ctc towards efficient and accurate scene text recognition"):
  with members:
    - "a simple and robust convolutional attention network for irregular text recognition"
    - "scene text recognition from two dimensional perspective"
    - "edit probability for scene text recognition"
    - "aon towards arbitrarily oriented text recognition"
    - "focusing attention towards accurate text recognition in natural images"
    - "recursive recurrent nets with attention modeling for ocr in the wild"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "a new perspective for flexible feature gathering in scene text recognition via character anchor pooling"):
  with members:
    - "scene text recognition from two dimensional perspective"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "scene text detection and recognition the deep learning era"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "scene text recognition with sliding convolutional character models"
    - "feature pyramid networks for object detection"
    - "synthetic data for text localisation in natural images"
    - "robust scene text recognition with automatic rectification"

Connected through hyperedge HE-8 (induced by source paper "towards end to end text spotting in natural scenes"):
  with members:
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "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"
    - "edit probability for scene text recognition"
    - "scene text detection with supervised pyramid context network"
    - "textboxes a single shot oriented scene text detector"
    - "esir end to end scene text recognition via iterative image rectification"

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)