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: "cascaded segmentation detection networks for word level text spotting"
  Abstract: We introduce an algorithm for word-level text spotting that is able to accurately and reliably determine the bounding regions of individual words of text "in the wild". Our system is formed by the cascade of two convolutional neural networks. The first network is fully convolutional and is in charge of detecting areas containing text. This results in a very reliable but possibly inaccurate segmentation of the input image. The second network (inspired by the popular YOLO architecture) analyzes each segment produced in the first stage, and predicts oriented rectangular regions containing individual words. No post-processing (e.g. text line grouping) is necessary. With execution time of 450 ms for a 1000-by-560 image on a Titan X GPU, our system achieves the highest score to date among published algorithms on the ICDAR 2015 Incidental Scene Text dataset benchmark.

Connected through hyperedge HE-1 (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"
    - "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"
    - "character proposal network for robust text extraction"
    - "text attentional convolutional neural network for scene text detection"

Connected through hyperedge HE-2 (induced by source paper "automatic semantic content removal by learning to neglect"):
  with members:
    - "generative image inpainting with contextual attention"
    - "attention is all you need"
    - "deep blind image inpainting"
    - "instance normalization the missing ingredient for fast stylization"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "multi oriented text detection with fully convolutional networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "adversarial feature learning"

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"
    - "deep direct regression for multi oriented scene text detection"
    - "detecting oriented text in natural images by linking segments"
    - "mask r cnn"
    - "character proposal network for robust text extraction"
    - "training region based object detectors with online hard example mining"

Connected through hyperedge HE-4 (induced by source paper "deep scene text detection with connected component proposals"):
  with members:
    - "multi oriented text detection with fully convolutional networks"
    - "ssd single shot multibox detector"
    - "stuffnet using stuff to improve object detection"
    - "textboxes a fast text detector with a single deep neural network"
    - "detecting text in natural image with connectionist text proposal network"
    - "deeptext a unified framework for text proposal generation and text detection in natural images"
    - "spatial transformer networks"
    - "parsenet looking wider to see better"

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)