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: "scene text recognition with sliding convolutional character models"
  Abstract: Scene text recognition has attracted great interests from the computer vision and pattern recognition community in recent years. State-of-the-art methods use concolutional neural networks (CNNs), recurrent neural networks with long short-term memory (RNN-LSTM) or the combination of them. In this paper, we investigate the intrinsic characteristics of text recognition, and inspired by human cognition mechanisms in reading texts, we propose a scene text recognition method with character models on convolutional feature map. The method simultaneously detects and recognizes characters by sliding the text line image with character models, which are learned end-to-end on text line images labeled with text transcripts. The character classifier outputs on the sliding windows are normalized and decoded with Connectionist Temporal Classification (CTC) based algorithm. Compared to previous methods, our method has a number of appealing properties: (1) It avoids the difficulty of character segmentation which hinders the performance of segmentation-based recognition methods; (2) The model can be trained simply and efficiently because it avoids gradient vanishing/exploding in training RNN-LSTM based models; (3) It bases on character models trained free of lexicon, and can recognize unknown words. (4) The recognition process is highly parallel and enables fast recognition. Our experiments on several challenging English and Chinese benchmarks, including the IIIT-5K, SVT, ICDAR03/13 and TRW15 datasets, demonstrate that the proposed method yields superior or comparable performance to state-of-the-art methods while the model size is relatively small.

Connected through hyperedge HE-1 (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"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "scene text detection and recognition the deep learning era"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "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-2 (induced by source paper "rethinking irregular scene text recognition"):
  with members:
    - "character region awareness for text detection"
    - "synthtext3d synthesizing scene text images from 3d virtual worlds"
    - "scene text recognition from two dimensional perspective"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "scene text detection and recognition the deep learning era"
    - "total text a comprehensive dataset for scene text detection and recognition"

Connected through hyperedge HE-3 (induced by source paper "aggregation cross entropy for sequence recognition"):
  with members:
    - "edit probability for scene text recognition"
    - "attention is all you need"
    - "focusing attention towards accurate text recognition in natural images"
    - "learning spatial semantic context with fully convolutional recurrent network for online handwritten chinese text recognition"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "hierarchical question image co attention for visual question answering"
    - "synthetic data for text localisation in natural images"

Connected through hyperedge HE-4 (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"
    - "focusing attention towards accurate text recognition in natural images"
    - "recursive recurrent nets with attention modeling for ocr in the wild"

Connected through hyperedge HE-5 (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"
    - "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"
    - "synthetic data for text localisation in natural images"

Connected through hyperedge HE-6 (induced by source paper "alchemy techniques for rectification based irregular scene text recognition"):
  with members:
    - "character region awareness for text detection"
    - "synthtext3d synthesizing scene text images from 3d virtual worlds"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "scene text detection and recognition the deep learning era"
    - "reading scene text with attention convolutional sequence modeling"
    - "detecting oriented text in natural images by linking segments"
    - "recursive recurrent nets with attention modeling for ocr in the wild"

Connected through hyperedge HE-7 (induced by source paper "shopsign a diverse scene text dataset of chinese shop signs in street views"):
  with members:
    - "chinese text in the wild"
    - "textboxes a single shot oriented scene text detector"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "east an efficient and accurate scene text detector"
    - "icdar2017 competition on reading chinese text in the wild rctw 17"
    - "detecting oriented text in natural images by linking segments"
    - "synthetic data for text localisation in natural images"

Connected through hyperedge HE-8 (induced by source paper "accurate data efficient unconstrained text recognition with convolutional neural networks"):
  with members:
    - "an efficient end to end neural model for handwritten text recognition"
    - "scene text recognition from two dimensional perspective"
    - "handwriting recognition of historical documents with few labeled data"
    - "autoaugment learning augmentation policies from data"
    - "attention u net learning where to look for the pancreas"
    - "how many labeled license plates are needed"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

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)