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: "chinese text in the wild"
  Abstract: We introduce Chinese Text in the Wild, a very large dataset of Chinese text in street view images. While optical character recognition (OCR) in document images is well studied and many commercial tools are available, detection and recognition of text in natural images is still a challenging problem, especially for more complicated character sets such as Chinese text. Lack of training data has always been a problem, especially for deep learning methods which require massive training data. #R##N#In this paper we provide details of a newly created dataset of Chinese text with about 1 million Chinese characters annotated by experts in over 30 thousand street view images. This is a challenging dataset with good diversity. It contains planar text, raised text, text in cities, text in rural areas, text under poor illumination, distant text, partially occluded text, etc. For each character in the dataset, the annotation includes its underlying character, its bounding box, and 6 attributes. The attributes indicate whether it has complex background, whether it is raised, whether it is handwritten or printed, etc. The large size and diversity of this dataset make it suitable for training robust neural networks for various tasks, particularly detection and recognition. We give baseline results using several state-of-the-art networks, including AlexNet, OverFeat, Google Inception and ResNet for character recognition, and YOLOv2 for character detection in images. Overall Google Inception has the best performance on recognition with 80.5% top-1 accuracy, while YOLOv2 achieves an mAP of 71.0% on detection. Dataset, source code and trained models will all be publicly available on the website.

Connected through hyperedge HE-1 (induced by source paper "chinese street view text large scale chinese text reading with partially supervised learning"):
  with members:
    - "rotation sensitive regression for oriented scene text detection"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "textboxes a single shot oriented scene text detector"
    - "exploring the limits of weakly supervised pretraining"
    - "textnet irregular text reading from images with an end to end trainable network"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"

Connected through hyperedge HE-2 (induced by source paper "icdar 2019 competition on large scale street view text with partial labeling rrc lsvt"):
  with members:
    - "pyramid mask text detector"
    - "an end to end textspotter with explicit alignment and attention"
    - "pixellink detecting scene text via instance segmentation"
    - "textboxes a single shot oriented scene text detector"
    - "deformable convnets v2 more deformable better results"
    - "path aggregation network for instance segmentation"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"

Connected through hyperedge HE-3 (induced by source paper "shopsign a diverse scene text dataset of chinese shop signs in street views"):
  with members:
    - "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"
    - "scene text recognition with sliding convolutional character models"
    - "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"

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)