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 "deep learning based isolated arabic scene character recognition"):
  Abstract: The technological advancement and sophistication in cameras and gadgets prompt researchers to have focus on image analysis and text understanding. The deep learning techniques demonstrated well to assess the potential for classifying text from natural scene images as reported in recent years. There are variety of deep learning approaches that prospects the detection and recognition of text, effectively from images. In this work, we presented Arabic scene text recognition using Convolutional Neural Networks (ConvNets) as a deep learning classifier. As the scene text data is slanted and skewed, thus to deal with maximum variations, we employ five orientations with respect to single occurrence of a character. The training is formulated by keeping filter size 3 x 3 and 5 x 5 with stride value as 1 and 2. During text classification phase, we trained network with distinct learning rates. Our approach reported encouraging results on recognition of Arabic characters from segmented Arabic scene images.

Includes members:
  Member "memory matters convolutional recurrent neural network for scene text recognition":
    also connected through other hyperedges:
      (none)
  Member "a fine grained approach to scene text script identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "patch aggregator for scene text script identification")
  Member "a cnn based scene chinese text recognition algorithm with synthetic data engine":
    also connected through other hyperedges:
      (none)
  Member "efficient scene text localization and recognition with local character refinement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "exploring the capacity of sequential free box discretization network for omnidirectional scene text detection")
  Member "harvesting discriminative meta objects with deep cnn features for scene classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "inspect transfer learning architecture with dilated convolution")
  Member "arabic text recognition in video sequences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unconstrained scene text and video text recognition for arabic script")

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)