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: "ocr of historical printings with an application to building diachronic corpora a case study using the ridges herbal corpus"
  Abstract: This article describes the results of a case study that applies Neural Network-based Optical Character Recognition (OCR) to scanned images of books printed between 1487 and 1870 by training the OCR engine OCRopus [@breuel2013high] on the RIDGES herbal text corpus [@OdebrechtEtAlSubmitted]. Training specific OCR models was possible because the necessary *ground truth* is available as error-corrected diplomatic transcriptions. The OCR results have been evaluated for accuracy against the ground truth of unseen test sets. Character and word accuracies (percentage of correctly recognized items) for the resulting machine-readable texts of individual documents range from 94% to more than 99% (character level) and from 76% to 97% (word level). This includes the earliest printed books, which were thought to be inaccessible by OCR methods until recently. Furthermore, OCR models trained on one part of the corpus consisting of books with different printing dates and different typesets *(mixed models)* have been tested for their predictive power on the books from the other part containing yet other fonts, mostly yielding character accuracies well above 90%. It therefore seems possible to construct generalized models trained on a range of fonts that can be applied to a wide variety of historical printings still giving good results. A moderate postcorrection effort of some pages will then enable the training of individual models with even better accuracies. Using this method, diachronic corpora including early printings can be constructed much faster and cheaper than by manual transcription. The OCR methods reported here open up the possibility of transforming our printed textual cultural heritage into electronic text by largely automatic means, which is a prerequisite for the mass conversion of scanned books.

Connected through hyperedge HE-1 (induced by source paper "ocr4all an open source tool providing a semi automatic ocr workflow for historical printings"):
  with members:
    - "a two stage method for text line detection in historical documents"
    - "evaluating sequence to sequence models for handwritten text recognition"
    - "calamari a high performance tensorflow based deep learning package for optical character recognition"
    - "improving ocr accuracy on early printed books by utilizing cross fold training and voting"
    - "state of the art optical character recognition of 19th century fraktur scripts using open source engines"
    - "fully convolutional neural networks for page segmentation of historical document images"
    - "ground truth for training ocr engines on historical documents in german fraktur and early modern latin"
    - "larex a semi automatic open source tool for layout analysis and region extraction on early printed books"

Connected through hyperedge HE-2 (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"
    - "case study of a highly automated layout analysis and ocr of an incunabulum der heiligen leben 1488"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "feature pyramid networks for object detection"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-3 (induced by source paper "improving ocr accuracy on early printed books by utilizing cross fold training and voting"):
  with members:
    - "case study of a highly automated layout analysis and ocr of an incunabulum der heiligen leben 1488"

Connected through hyperedge HE-4 (induced by source paper "larex a semi automatic open source tool for layout analysis and region extraction on early printed books"):
  with members:
    - "case study of a highly automated layout analysis and ocr of an incunabulum der heiligen leben 1488"
    - "automatic quality evaluation and semi automatic improvement of ocr models for historical printings"

Connected through hyperedge HE-5 (induced by source paper "case study of a highly automated layout analysis and ocr of an incunabulum der heiligen leben 1488"):
  with members:
    - "larex a semi automatic open source tool for layout analysis and region extraction on early printed books"
    - "automatic quality evaluation and semi automatic improvement of ocr models for historical printings"

Connected through hyperedge HE-6 (induced by source paper "profiling of ocr ed historical texts revisited"):
  with members:
    - "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.CL(Computation and Language)