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: "document image binarization with fully convolutional neural networks"
  Abstract: Binarization of degraded historical manuscript images is an important pre-processing step for many document processing tasks. We formulate binarization as a pixel classification learning task and apply a novel Fully Convolutional Network (FCN) architecture that operates at multiple image scales, including full resolution. The FCN is trained to optimize a continuous version of the Pseudo F-measure metric and an ensemble of FCNs outperform the competition winners on 4 of 7 DIBCO competitions. This same binarization technique can also be applied to different domains such as Palm Leaf Manuscripts with good performance. We analyze the performance of the proposed model w.r.t. the architectural hyperparameters, size and diversity of training data, and the input features chosen.

Connected through hyperedge HE-1 (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"
    - "a selectional auto encoder approach for document image binarization"
    - "robust regression for image binarization under heavy noise and nonuniform background"
    - "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-2 (induced by source paper "deeperase weakly supervised ink artifact removal in document text images"):
  with members:
    - "character based handwritten text transcription with attention networks"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"
    - "detecting text in natural image with connectionist text proposal network"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "deepotsu document enhancement and binarization using iterative deep learning"):
  with members:
    - "a selectional auto encoder approach for document image binarization"
    - "robust regression for image binarization under heavy noise and nonuniform background"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "chargrid towards understanding 2d documents"):
  with members:
    - "focal loss for dense object detection"
    - "minimum word error rate training for attention based sequence to sequence models"
    - "cloudscan a configuration free invoice analysis system using recurrent neural networks"
    - "learning to extract semantic structure from documents using multimodal fully convolutional neural network"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "neural architectures for named entity recognition"
    - "multilingual language processing from bytes"
    - "a neural attention model for abstractive sentence summarization"

Connected through hyperedge HE-5 (induced by source paper "recognizing challenging handwritten annotations with fully convolutional networks"):
  with members:
    - "real time document image classification using deep cnn and extreme learning machines"
    - "sequence to label script identification for multilingual ocr"
    - "cutting the error by half investigation of very deep cnn and advanced training strategies for document image classification"
    - "pyramid scene parsing network"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "improving document binarization via adversarial noise texture augmentation"):
  with members:
    - "staff line removal using generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "texture synthesis using convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "pagenet page boundary extraction in historical handwritten documents"):
  with members:
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "spatial transformer networks"
    - "conditional random fields as recurrent neural networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "fully convolutional networks for 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)