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: "icdar2017 competition on reading chinese text in the wild rctw 17"
  Abstract: Chinese is the most widely used language in the world. Algorithms that read Chinese text in natural images facilitate applications of various kinds. Despite the large potential value, datasets and competitions in the past primarily focus on English, which bares very different characteristics than Chinese. This report introduces RCTW, a new competition that focuses on Chinese text reading. The competition features a large-scale dataset with 12,263 annotated images. Two tasks, namely text localization and end-to-end recognition, are set up. The competition took place from January 20 to May 31, 2017. 23 valid submissions were received from 19 teams. This report includes dataset description, task definitions, evaluation protocols, and results summaries and analysis. Through this competition, we call for more future research on the Chinese text reading problem. The official website for the competition is this http URL

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 "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-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 "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"

Connected through hyperedge HE-5 (induced by source paper "gliding vertex on the horizontal bounding box for multi oriented object 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"
    - "centernet keypoint triplets for object detection"
    - "cad net a context aware detection network for objects in remote sensing imagery"
    - "bottom up object detection by grouping extreme and center points"
    - "cornernet detecting objects as paired keypoints"
    - "rotation sensitive regression for oriented scene text detection"

Connected through hyperedge HE-6 (induced by source paper "look more than once an accurate detector for text of arbitrary shapes"):
  with members:
    - "inceptext a new inception text module with deformable psroi pooling for multi oriented scene text detection"
    - "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"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "sliding line point regression for shape robust scene text detection"

Connected through hyperedge HE-7 (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"
    - "chinese text in the wild"
    - "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"

Connected through hyperedge HE-8 (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"

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)