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: "supervised mid level features for word image representation"
  Abstract: This paper addresses the problem of learning word image representations: given the cropped image of a word, we are interested in finding a descriptive, robust, and compact fixed-length representation. Machine learning techniques can then be supplied with these representations to produce models useful for word retrieval or recognition tasks. Although many works have focused on the machine learning aspect once a global representation has been produced, little work has been devoted to the construction of those base image representations: most works use standard coding and aggregation techniques directly on top of standard computer vision features such as SIFT or HOG. #R##N#We propose to learn local mid-level features suitable for building word image representations. These features are learnt by leveraging character bounding box annotations on a small set of training images. However, contrary to other approaches that use character bounding box information, our approach does not rely on detecting the individual characters explicitly at testing time. Our local mid-level features can then be aggregated to produce a global word image signature. When pairing these features with the recent word attributes framework of Almaz\'an et al., we obtain results comparable with or better than the state-of-the-art on matching and recognition tasks using global descriptors of only 96 dimensions.

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 "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"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "ga dan geometry aware domain adaptation network for scene text detection and recognition"):
  with members:
    - "adaptive composition gan towards realistic image synthesis"
    - "scene text synthesis for efficient and effective deep network training"
    - "rotation sensitive regression for oriented scene text detection"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "fots fast oriented text spotting with a unified network"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"

Connected through hyperedge HE-4 (induced by source paper "scene text synthesis for efficient and effective deep network training"):
  with members:
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "spatial fusion gan for image synthesis"
    - "esir end to end scene text recognition via iterative image rectification"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "cycada cycle consistent adversarial domain adaptation"
    - "single shot text detector with regional attention"

Connected through hyperedge HE-5 (induced by source paper "2d attentional irregular scene text recognizer"):
  with members:
    - "scene text recognition from two dimensional perspective"
    - "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"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "adaptive embedding gate for attention based scene text recognition"):
  with members:
    - "multi oriented text detection and verification in video frames and scene images"
    - "edit probability for scene text recognition"
    - "esir end to end scene text recognition via iterative image rectification"
    - "scene text detection and recognition the deep learning era"
    - "a simple neural network module for relational reasoning"
    - "aon towards arbitrarily oriented text recognition"
    - "scene text recognition with sliding convolutional character models"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-7 (induced by source paper "symmetry constrained rectification network for scene text recognition"):
  with members:
    - "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"
    - "scene text detection and recognition the deep learning era"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "a multi object rectified attention network for scene text recognition"):
  with members:
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "aon towards arbitrarily oriented text recognition"
    - "scene text recognition with sliding convolutional character models"
    - "detecting curve text in the wild new dataset and new solution"
    - "deformable convolutional networks"
    - "focusing attention towards accurate text recognition in natural images"
    - "generative shape models joint text recognition and segmentation with very little training data"
    - "recursive recurrent nets with attention modeling for ocr in the wild"

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)