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: "open evaluation tool for layout analysis of document images"
  Abstract: This paper presents an open tool for standardizing the evaluation process of the layout analysis task of document images at pixel level. We introduce a new evaluation tool that is both available as a standalone Java application and as a RESTful web service. This evaluation tool is free and open-source in order to be a common tool that anyone can use and contribute to. It aims at providing as many metrics as possible to investigate layout analysis predictions, and also provides an easy way of visualizing the results. This tool evaluates document segmentation at pixel level, and supports multi-labeled pixel ground truth. Finally, this tool has been successfully used for the ICDAR 2017 competition on Layout Analysis for Challenging Medieval Manuscripts.

Connected through hyperedge HE-1 (induced by source paper "unsupervised text line segmentation"):
  with members:
    - "a two stage method for text line detection in historical documents"
    - "badam a public dataset for baseline detection in arabic script manuscripts"
    - "labeling cutting grouping an efficient text line segmentation method for medieval manuscripts"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "unsupervised natural image patch learning"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis"):
  with members:
    - "labeling cutting grouping an efficient text line segmentation method for medieval manuscripts"
    - "do better imagenet models transfer better"
    - "offline signature verification by combining graph edit distance and triplet networks"
    - "deep learning for classical japanese literature"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "identifying cross depicted historical motifs"
    - "historical document image segmentation with lda initialized deep neural networks"
    - "transforming sensor data to the image domain for deep learning an application to footstep detection"

Connected through hyperedge HE-3 (induced by source paper "labeling cutting grouping an efficient text line segmentation method for medieval manuscripts"):
  with members:
    - "icdar 2019 historical document reading challenge on large structured chinese family records"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "historical document image segmentation with lda initialized deep neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "historical document image segmentation with lda initialized deep neural networks"):
  with members:
    - "what you expect is not what you get questioning reconstruction classification correlation of stacked convolutional auto encoder features"
    - "pca initialized deep neural networks applied to document image analysis"
    - "deep learning in neural networks"
    - "deep linear discriminant analysis"
    - "data dependent initializations of convolutional neural networks"

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)