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: "efficient scene text localization and recognition with local character refinement"
  Abstract: An unconstrained end-to-end text localization and recognition method is presented. The method detects initial text hypothesis in a single pass by an efficient region-based method and subsequently refines the text hypothesis using a more robust local text model, which deviates from the common assumption of region-based methods that all characters are detected as connected components. #R##N#Additionally, a novel feature based on character stroke area estimation is introduced. The feature is efficiently computed from a region distance map, it is invariant to scaling and rotations and allows to efficiently detect text regions regardless of what portion of text they capture. #R##N#The method runs in real time and achieves state-of-the-art text localization and recognition results on the ICDAR 2013 Robust Reading dataset.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a pooling based scene text proposal technique for scene text reading in the wild"):
  with members:
    - "an end to end textspotter with explicit alignment and attention"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "wordfence text detection in natural images with border awareness"
    - "icdar2017 competition on reading chinese text in the wild rctw 17"
    - "wetext scene text detection under weak supervision"
    - "deep direct regression for multi oriented scene text detection"
    - "r2cnn rotational region cnn for orientation robust scene text detection"

Connected through hyperedge HE-3 (induced by source paper "multi oriented scene text detection via corner localization and region segmentation"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "wordsup exploiting word annotations for character based text detection"
    - "east an efficient and accurate scene text detector"
    - "single shot text detector with regional attention"
    - "dssd deconvolutional single shot detector"
    - "wetext scene text detection under weak supervision"
    - "integrating scene text and visual appearance for fine grained image classification"

Connected through hyperedge HE-4 (induced by source paper "multi oriented text detection and verification in video frames and scene images"):
  with members:
    - "date field retrieval in scene image and video frames using text enhancement and shape coding"
    - "text recognition in scene image and video frame using color channel selection"
    - "text flow a unified text detection system in natural scene images"
    - "text attentional convolutional neural network for scene text detection"
    - "multi oriented text detection with fully convolutional networks"
    - "reading text in the wild with convolutional neural networks"
    - "robust text detection in natural scene images"

Connected through hyperedge HE-5 (induced by source paper "single shot text detector with regional attention"):
  with members:
    - "deep matching prior network toward tighter multi oriented text detection"
    - "east an efficient and accurate scene text detector"
    - "detecting oriented text in natural images by linking segments"
    - "text flow a unified text detection system in natural scene images"
    - "text attentional convolutional neural network for scene text detection"
    - "multi oriented text detection with fully convolutional networks"
    - "ssd single shot multibox detector"
    - "accurate text localization in natural image with cascaded convolutional text network"

Connected through hyperedge HE-6 (induced by source paper "detecting oriented text in natural images by linking segments"):
  with members:
    - "multi oriented text detection with fully convolutional networks"
    - "ssd single shot multibox detector"
    - "training region based object detectors with online hard example mining"
    - "scene text detection via holistic multi channel prediction"
    - "synthetic data for text localisation in natural images"
    - "detecting text in natural image with connectionist text proposal network"
    - "you only look once unified real time object detection"
    - "fast r cnn"

Connected through hyperedge HE-7 (induced by source paper "deep learning based isolated arabic scene character recognition"):
  with members:
    - "memory matters convolutional recurrent neural network for scene text recognition"
    - "a fine grained approach to scene text script identification"
    - "a cnn based scene chinese text recognition algorithm with synthetic data engine"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "arabic text recognition in video sequences"

Connected through hyperedge HE-8 (induced by source paper "detecting text in natural image with connectionist text proposal network"):
  with members:
    - "text flow a unified text detection system in natural scene images"
    - "text attentional convolutional neural network for scene text detection"
    - "multi oriented text detection with fully convolutional networks"
    - "accurate text localization in natural image with cascaded convolutional text network"
    - "synthetic data for text localisation in natural images"
    - "reading scene text in deep convolutional sequences"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal 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)