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Center node: "automatic quality evaluation and semi automatic improvement of mixed models for ocr on historical documents"
  Abstract: Good OCR results on historical documents rely on diplomatic transcriptions of printed material as ground truth which is both a scarce resource and time-consuming to generate. A strategy is proposed which starts from a mixed model trained on already available transcriptions from different centuries giving accuracies over 90% on a test set from the same period of time, overcoming the typography barrier of having to train individual models separately for each historical typeface. It is shown that both mean character confidence (as output by the OCR engine OCRopus) and lexicality (a measure of correctness of OCR tokens compared to a lexicon of modern wordforms taking historical spelling patterns into account, which can be calculated for any OCR engine) correlate with true accuracy determined from a comparison of OCR results with ground truth. These measures are then used to guide the training of new individual OCR models either using OCR prediction as pseudo ground truth (fully automatic method) or choosing a minimum set of hand-corrected lines as training material (manual method). Already 40-80 hand- corrected lines lead to OCR results with character error rates of only a few percent. This procedure minimizes the amount of ground truth production and does not depend on the previous construction of a specific typographic model.

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    - "a two stage method for text line detection in historical documents"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    - "fully convolutional neural networks for page segmentation of historical document images"
    - "ground truth for training ocr engines on historical documents in german fraktur and early modern latin"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"

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.DL(Digital Libraries)