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Center node: "a two stage method for text line detection in historical documents"
  Abstract: This work presents a two-stage text line detection method for historical documents. Each detected text line is represented by its baseline. In a first stage, a deep neural network called ARU-Net labels pixels to belong to one of the three classes: baseline, separator and other. The separator class marks beginning and end of each text line. The ARU-Net is trainable from scratch with manageably few manually annotated example images (#N#                  #N#                    #N#                  #N#                  $$<\,50$$#N#                  #N#                    #N#                  #N#                ). This is achieved by utilizing data augmentation strategies. The network predictions are used as input for the second stage which performs a bottom-up clustering to build baselines. The developed method is capable of handling complex layouts as well as curved and arbitrarily oriented text lines. It substantially outperforms current state-of-the-art approaches. For example, for the complex track of the cBAD: ICDAR2017 Competition on Baseline Detection the F value is increased from 0.859 to 0.922. The framework to train and run the ARU-Net is open source.

Connected through hyperedge HE-1 (induced by source paper "unsupervised text line segmentation"):
  with members:
    - "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"
    - "open evaluation tool for layout analysis of document images"

Connected through hyperedge HE-2 (induced by source paper "deep visual template free form parsing"):
  with members:
    - "linknet relational embedding for scene graph"
    - "group normalization"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "attention is all you need"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mask r cnn"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-3 (induced by source paper "ocr4all an open source tool providing a semi automatic ocr workflow for historical printings"):
  with members:
    - "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"
    - "profiling of ocr ed historical texts revisited"

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)