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: "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
  Abstract: Recently, models based on deep neural networks have dominated the fields of scene text detection and recognition. In this paper, we investigate the problem of scene text spotting, which aims at simultaneous text detection and recognition in natural images. An end-to-end trainable neural network model for scene text spotting is proposed. The proposed model, named as Mask TextSpotter, is inspired by the newly published work Mask R-CNN. Different from previous methods that also accomplish text spotting with end-to-end trainable deep neural networks, Mask TextSpotter takes advantage of simple and smooth end-to-end learning procedure, in which precise text detection and recognition are acquired via semantic segmentation. Moreover, it is superior to previous methods in handling text instances of irregular shapes, for example, curved text. Experiments on ICDAR2013, ICDAR2015 and Total-Text demonstrate that the proposed method achieves state-of-the-art results in both scene text detection and end-to-end text recognition tasks.

Connected through hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks"):
  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"
    - "character region awareness for text detection"
    - "deep visual template free form parsing"
    - "msr multi scale shape regression for scene text detection"
    - "rethinking table recognition using graph neural networks"

Connected through hyperedge HE-2 (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-3 (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-4 (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"
    - "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-5 (induced by source paper "a multi oriented chinese keyword spotter guided by text line detection"):
  with members:
    - "textboxes a single shot oriented scene text detector"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural network"
    - "query by string word spotting based on character bi gram indexing"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "reading text in the wild with convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "exploring the capacity of sequential free box discretization network for omnidirectional scene text detection"):
  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"
    - "character region awareness for text detection"
    - "region proposal by guided anchoring"
    - "msr multi scale shape regression for scene text detection"
    - "mask scoring r cnn"

Connected through hyperedge HE-7 (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-8 (induced by source paper "a feasible framework for arbitrary shaped scene text recognition"):
  with members:
    - "centernet keypoint triplets for object detection"
    - "efficientdet scalable and efficient object detection"
    - "icdar2019 robust reading challenge on arbitrary shaped text rrc art"
    - "fcos fully convolutional one stage object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "cornernet detecting objects as paired keypoints"
    - "fots fast oriented text spotting with a unified network"
    - "yolov3 an incremental improvement"

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)