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 "ocr4all an open source tool providing a semi automatic ocr workflow for historical printings"):
  Abstract: Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout recognition and segmentation, character recognition and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. A comfortable GUI allows error corrections not only in the final output, but already in early stages to minimize error propagations. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. Experiments showed that users with minimal or no experience were able to capture the text of even the earliest printed books with manageable effort and great quality, achieving excellent character error rates (CERs) below 0.5%. The fully automated application on 19th century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.

Includes members:
  Member "a two stage method for text line detection in historical documents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised text line segmentation")
  Member "evaluating sequence to sequence models for handwritten text recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised writer adaptation for synthetic to real handwritten word recognition")
  Member "calamari a high performance tensorflow based deep learning package for optical character recognition":
    also connected through other hyperedges:
      (none)
  Member "improving ocr accuracy on early printed books by utilizing cross fold training and voting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transfer learning for ocropus model training on early printed books")
  Member "state of the art optical character recognition of 19th century fraktur scripts using open source engines":
    also connected through other hyperedges:
      (none)
  Member "fully convolutional neural networks for page segmentation of historical document images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers")
  Member "ground truth for training ocr engines on historical documents in german fraktur and early modern latin":
    also connected through other hyperedges:
      (none)
  Member "larex a semi automatic open source tool for layout analysis and region extraction on early printed books":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "case study of a highly automated layout analysis and ocr of an incunabulum der heiligen leben 1488")

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)