human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "houghnet neural network architecture for vanishing points detection"):
  Abstract: In this paper we introduce a novel neural network architecture based on Fast Hough Transform layer. The layer of this type allows our neural network to accumulate features from linear areas across the entire image instead of local areas. We demonstrate its potential by solving the problem of vanishing points detection in the images of documents. Such problem occurs when dealing with camera shots of the documents in uncontrolled conditions. In this case, the document image can suffer several specific distortions including projective transform. To train our model, we use MIDV-500 dataset and provide testing results. The strong generalization ability of the suggested method is proven with its applying to a completely different ICDAR 2011 dewarping contest. In previously published papers considering these dataset authors measured the quality of vanishing point detection by counting correctly recognized words with open OCR engine Tesseract. To compare with them, we reproduce this experiment and show that our method outperforms the state-of-the-art result.

Includes members:
  Member "midv 500 a dataset for identity document analysis and recognition on mobile devices in video stream":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "face detection in camera captured images of identity documents under challenging conditions")
  Member "deep learning for vanishing point detection using an inverse gnomonic projection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "consac robust multi model fitting by conditional sample consensus")
  Member "vpgnet vanishing point guided network for lane and road marking detection and recognition":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review")
  Member "detecting vanishing points using global image context in a non manhattan world":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "vanishing point detection with direct and transposed fast hough transform inside the neural network")
  Member "robust scene text recognition with automatic rectification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")

Question: Please tell me which class the source paper 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)