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: "case study of a highly automated layout analysis and ocr of an incunabulum der heiligen leben 1488"
  Abstract: This paper provides the first thorough documentation of a high quality digitization process applied to an early printed book from the incunabulum period (1450-1500). The entire OCR related workflow including preprocessing, layout analysis and text recognition is illustrated in detail using the example of 'Der Heiligen Leben', printed in Nuremberg in 1488. For each step the required time expenditure was recorded. The character recognition yielded excellent results both on character (97.57%) and word (92.19%) level. Furthermore, a comparison of a highly automated (LAREX) and a manual (Aletheia) method for layout analysis was performed. By considerably automating the segmentation the required human effort was reduced significantly from over 100 hours to less than six hours, resulting in only a slight drop in OCR accuracy. Realistic estimates for the human effort necessary for full text extraction from incunabula can be derived from this study. The printed pages of the complete work together with the OCR result is available online ready to be inspected and downloaded.

Connected through hyperedge HE-1 (induced by source paper "indiscapes instance segmentation networks for layout parsing of historical indic manuscripts"):
  with members:
    - "fully convolutional neural networks for page segmentation of historical document images"
    - "focal loss for dense object detection"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "feature pyramid networks for object detection"
    - "ocr of historical printings with an application to building diachronic corpora a case study using the ridges herbal corpus"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "improving ocr accuracy on early printed books by utilizing cross fold training and voting"):
  with members:
    - "ocr of historical printings with an application to building diachronic corpora a case study using the ridges herbal corpus"

Connected through hyperedge HE-3 (induced by source paper "larex a semi automatic open source tool for layout analysis and region extraction on early printed books"):
  with members:
    - "automatic quality evaluation and semi automatic improvement of ocr models for historical printings"
    - "ocr of historical printings with an application to building diachronic corpora a case study using the ridges herbal corpus"

Connected through hyperedge HE-4 (induced by source paper "transfer learning for ocropus model training on early printed books"):
  with members:
    - "improving ocr accuracy on early printed books by utilizing cross fold training and voting"
    - "automatic quality evaluation and semi automatic improvement of ocr models for historical printings"

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)