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: "convolutional character networks"
  Abstract: Recent progress has been made on developing a unified framework for joint text detection and recognition in natural images, but existing joint models were mostly built on two-stage framework by involving ROI pooling, which can degrade the performance on recognition task. In this work, we propose convolutional character networks, referred as CharNet, which is an one-stage model that can process two tasks simultaneously in one pass. CharNet directly outputs bounding boxes of words and characters, with corresponding character labels. We utilize character as basic element, allowing us to overcome the main difficulty of existing approaches that attempted to optimize text detection jointly with a RNN-based recognition branch. In addition, we develop an iterative character detection approach able to transform the ability of character detection learned from synthetic data to real-world images. These technical improvements result in a simple, compact, yet powerful one-stage model that works reliably on multi-orientation and curved text. We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-the-art approaches [25, 24] by a large margin, e.g., with improvements of 65.33%->71.08% (with generic lexicon) on ICDAR 2015, and 54.0%->69.23% on Total-Text, on end-to-end text recognition. Code is available at: this https URL.

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"
    - "character region awareness for text detection"
    - "towards end to end text spotting in natural scenes"
    - "fcos fully convolutional one stage object detection"
    - "efficient and accurate arbitrary shaped text detection with pixel aggregation network"

Connected through hyperedge HE-2 (induced by source paper "puzzlenet scene text detection by segment context graph learning"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "character region awareness for text detection"
    - "shape robust text detection with progressive scale expansion network"
    - "rotation sensitive regression for oriented scene text detection"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "sa text simple but accurate detector for text of arbitrary shapes"):
  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"
    - "character region awareness for text detection"
    - "efficient and accurate arbitrary shaped text detection with pixel aggregation network"
    - "towards robust curve text detection with conditional spatial expansion"
    - "shape robust text detection with progressive scale expansion network"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"

Connected through hyperedge HE-4 (induced by source paper "ienet interacting embranchment one stage anchor free detector for orientation aerial object detection"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "character region awareness for text detection"
    - "fcos fully convolutional one stage object detection"
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "scene text detection with supervised pyramid context network"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-5 (induced by source paper "chargrid ocr end to end trainable optical character recognition for printed documents using instance segmentation"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "yolov3 an incremental improvement"
    - "chargrid towards understanding 2d documents"
    - "focal loss for dense object detection"
    - "east an efficient and accurate scene text detector"
    - "aon towards arbitrarily oriented text recognition"
    - "mask r cnn"
    - "recursive recurrent nets with attention modeling for ocr in the wild"

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)