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: "a fast hierarchical method for multi script and arbitrary oriented scene text extraction"
  Abstract: Typography and layout lead to the hierarchical organisation of text in words, text lines, paragraphs. This inherent structure is a key property of text in any script and language, which has nonetheless been minimally leveraged by existing text detection methods. This paper addresses the problem of text segmentation in natural scenes from a hierarchical perspective. Contrary to existing methods, we make explicit use of text structure, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypotheses with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Results obtained over four standard datasets, covering text in variable orientations and different languages, demonstrate that our algorithm, while being trained in a single mixed dataset, outperforms state of the art methods in unconstrained scenarios.

Connected through hyperedge HE-1 (induced by source paper "an anchor free region proposal network for faster r cnn based text detection approaches"):
  with members:
    - "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"
    - "pixellink detecting scene text via instance segmentation"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "textproposals a text specific selective search algorithm for word spotting in the wild"):
  with members:
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "reading text in the wild with convolutional neural networks"
    - "robust text detection in natural scene images"
    - "end to end text recognition with hybrid hmm maxout models"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "obtaining calibrated probabilities from boosting"

Connected through hyperedge HE-3 (induced by source paper "improving patch based scene text script identification with ensembles of conjoined networks"):
  with members:
    - "a fine grained approach to scene text script identification"
    - "sparse radial sampling lbp for writer identification"
    - "automatic script identification in the wild"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-4 (induced by source paper "a fine grained approach to scene text script identification"):
  with members:
    - "sparse radial sampling lbp for writer identification"
    - "automatic script identification in the wild"

Connected through hyperedge HE-5 (induced by source paper "object proposals for text extraction in the wild"):
  with members:
    - "reading text in the wild with convolutional neural networks"
    - "how good are detection proposals really"

Connected through hyperedge HE-6 (induced by source paper "reading text in the wild with convolutional neural networks"):
  with members:
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "fast edge detection using structured forests"
    - "robust text detection in natural scene images"
    - "supervised mid level features for word image representation"
    - "end to end text recognition with hybrid hmm maxout models"
    - "multi digit number recognition from street view imagery using deep convolutional neural networks"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)