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: "character proposal network for robust text extraction"
  Abstract: Maximally stable extremal regions (MSER), which is a popular method to generate character proposals/candidates, has shown superior performance in scene text detection. However, the pixel-level operation limits its capability for handling some challenging cases (e.g., multiple connected characters, separated parts of one character and non-uniform illumination). To better tackle these cases, we design a character proposal network (CPN) by taking advantage of the high capacity and fast computing of fully convolutional network (FCN). Specifically, the network simultaneously predicts characterness scores and refines the corresponding locations. The characterness scores can be used for proposal ranking to reject non-character proposals and the refining process aims to obtain the more accurate locations. Furthermore, considering the situation that different characters have different aspect ratios, we propose a multi-template strategy, designing a refiner for each aspect ratio. The extensive experiments indicate our method achieves recall rates of 93.88%, 93.60% and 96.46% on ICDAR 2013, SVT and Chinese2k datasets respectively using less than 1000 proposals, demonstrating promising performance of our character proposal network.

Connected through hyperedge HE-1 (induced by source paper "selective distillation of weakly annotated gtd for vision based slab identification system"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "annotating object instances with a polygon rnn"
    - "detecting oriented text in natural images by linking segments"
    - "multi oriented text detection with fully convolutional networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "detecting text in natural image with connectionist text proposal network"

Connected through hyperedge HE-2 (induced by source paper "arbitrary oriented scene text detection via rotation proposals"):
  with members:
    - "deep matching prior network toward tighter multi oriented text detection"
    - "improving text proposals for scene images with fully convolutional networks"
    - "cascaded segmentation detection networks for word level text spotting"
    - "integrating scene text and visual appearance for fine grained image classification"
    - "detecting oriented text in natural images by linking segments"
    - "evolving boxes for fast vehicle detection"
    - "text flow a unified text detection system in natural scene images"
    - "text attentional convolutional neural network for scene text detection"

Connected through hyperedge HE-3 (induced by source paper "fused text segmentation networks for multi oriented scene text detection"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "cascaded segmentation detection networks for word level text spotting"
    - "deep direct regression for multi oriented scene text detection"
    - "detecting oriented text in natural images by linking segments"
    - "mask r cnn"
    - "training region based object detectors with online hard example mining"

Connected through hyperedge HE-4 (induced by source paper "deeptext a unified framework for text proposal generation and text detection in natural images"):
  with members:
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "reading text in the wild with convolutional neural networks"

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)