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 selectional auto encoder approach for document image binarization"
  Abstract: Abstract   Binarization plays a key role in the automatic information retrieval from document images. This process is usually performed in the first stages of document analysis systems, and serves as a basis for subsequent steps. Hence it has to be robust in order to allow the full analysis workflow to be successful. Several methods for document image binarization have been proposed so far, most of which are based on hand-crafted image processing strategies. Recently, Convolutional Neural Networks have shown an amazing performance in many disparate duties related to computer vision. In this paper we discuss the use of convolutional auto-encoders devoted to learning an end-to-end map from an input image to its selectional output, in which activations indicate the likelihood of pixels to be either foreground or background. Once trained, documents can therefore be binarized by parsing them through the model and applying a global threshold. This approach has proven to outperform existing binarization strategies in a number of document types.

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"
    - "deepotsu document enhancement and binarization using iterative deep learning"
    - "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:
    - "deepotsu document enhancement and binarization using iterative deep learning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "deepotsu document enhancement and binarization using iterative deep learning"):
  with members:
    - "robust regression for image binarization under heavy noise and nonuniform background"
    - "document image binarization with fully convolutional neural networks"
    - "densely connected convolutional networks"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "fully convolutional networks for semantic segmentation"
    - "beyond pixels and regions a non local patch means nlpm method for content level restoration enhancement and reconstruction of degraded document images"

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)