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: "deepotsu document enhancement and binarization using iterative deep learning"
  Abstract: Abstract   This paper presents a novel iterative deep learning framework and applies it to document enhancement and binarization. Unlike the traditional methods that predict the binary label of each pixel on the input image, we train the neural network to learn the degradations in document images and produce uniform images of the degraded input images, which in turn allows the network to refine the output iteratively. Two different iterative methods have been studied in this paper: recurrent refinement (RR) that uses the same trained neural network in each iteration for document enhancement and stacked refinement (SR) that uses a stack of different neural networks for iterative output refinement. Given the learned nature of the uniform and enhanced image, the binarization map can be easily obtained through use of a global or local threshold. The experimental results on several public benchmark data sets show that our proposed method provides a new, clean version of the degraded image, one that is suitable for visualization and which shows promising results for binarization using Otsu’s global threshold, based on enhanced images learned iteratively by the neural network.

Connected through hyperedge HE-1 (induced by source paper "adaptive binarization based on fuzzy integrals"):
  with members:
    - "deep learning in video multi object tracking a survey"
    - "road crack detection using deep convolutional neural network and adaptive thresholding"
    - "a selectional auto encoder approach for document image binarization"
    - "deeply supervised salient object detection with short connections"
    - "semantic binary segmentation using convolutional networks without decoders"
    - "convolutional feature masking for joint object and stuff segmentation"

Connected through hyperedge HE-2 (induced by source paper "binet degraded manuscript binarization in diverse document textures and layouts using deep encoder decoder networks"):
  with members:
    - "a selectional auto encoder approach for document image binarization"
    - "robust regression for image binarization under heavy noise and nonuniform background"
    - "document image binarization with fully convolutional neural networks"
    - "densely connected convolutional networks"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "grid long short term memory"

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)