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 "unsupervised text line segmentation"):
  Abstract: We present an unsupervised text line segmentation method that is inspired by the relative variance between text lines and spaces among text lines. Handwritten text line segmentation is important for the efficiency of further processing. A common method is to train a deep learning network for embedding the document image into an image of blob lines that are tracing the text lines. Previous methods learned such embedding in a supervised manner, requiring the annotation of many document images. This paper presents an unsupervised embedding of document image patches without a need for annotations. The main idea is that the number of foreground pixels over the text lines is relatively different from the number of foreground pixels over the spaces among text lines. Generating similar and different pairs relying on this principle definitely leads to outliers. However, as the results show, the outliers do not harm the convergence and the network learns to discriminate the text lines from the spaces between text lines. We experimented with a challenging Arabic handwritten text line segmentation dataset, VML-AHTE, and achieved a superior performance even over the supervised methods.

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  Member "a two stage method for text line detection in historical documents":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 "deep visual template free form parsing")
  Member "badam a public dataset for baseline detection in arabic script manuscripts":
    also connected through other hyperedges:
      (none)
  Member "labeling cutting grouping an efficient text line segmentation method for medieval manuscripts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis")
  Member "dhsegment a generic deep learning approach for document segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combining visual and textual features for semantic segmentation of historical newspapers")
  Member "unsupervised natural image patch learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uninformed students student teacher anomaly detection with discriminative latent embeddings")
  Member "open evaluation tool for layout analysis of document images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis")

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)