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: "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
  Abstract: Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed. Compared with previous systems for scene text recognition, the proposed architecture possesses four distinctive properties: (1) It is end-to-end trainable, in contrast to most of the existing algorithms whose components are separately trained and tuned. (2) It naturally handles sequences in arbitrary lengths, involving no character segmentation or horizontal scale normalization. (3) It is not confined to any predefined lexicon and achieves remarkable performances in both lexicon-free and lexicon-based scene text recognition tasks. (4) It generates an effective yet much smaller model, which is more practical for real-world application scenarios. The experiments on standard benchmarks, including the IIIT-5K, Street View Text and ICDAR datasets, demonstrate the superiority of the proposed algorithm over the prior arts. Moreover, the proposed algorithm performs well in the task of image-based music score recognition, which evidently verifies the generality of it.

Connected through hyperedge HE-1 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "convolutional character networks"
    - "character region awareness for text detection"
    - "towards end to end text spotting in natural scenes"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-2 (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-3 (induced by source paper "text perceptron towards end to end arbitrary shaped text spotting"):
  with members:
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "towards robust curve text detection with conditional spatial expansion"
    - "shape robust text detection with progressive scale expansion network"
    - "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"

Connected through hyperedge HE-4 (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-5 (induced by source paper "fine grained image classification and retrieval by combining visual and locally pooled textual features"):
  with members:
    - "towards vqa models that can read"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "on the variance of the adaptive learning rate and beyond"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "lookahead optimizer k steps forward 1 step back"
    - "single shot scene text retrieval"
    - "an end to end textspotter with explicit alignment and attention"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-6 (induced by source paper "swaptext image based texts transfer in scenes"):
  with members:
    - "controllable artistic text style transfer via shape matching gan"
    - "ga dan geometry aware domain adaptation network for scene text detection and recognition"
    - "symmetry constrained rectification network for scene text recognition"
    - "arbitrary style transfer with style attentional networks"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "spatial fusion gan for image synthesis"
    - "image inpainting via generative multi column convolutional neural networks"

Connected through hyperedge HE-7 (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-8 (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"
    - "show attend and read a simple and strong baseline for irregular 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"

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)