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Center node: "text line segmentation of historical documents a survey"
  Abstract: There is a huge amount of historical documents in libraries and in various National Archives that have not been exploited electronically. Although automatic reading of complete pages remains, in most cases, a long-term objective, tasks such as word spotting, text/image alignment, authentication and extraction of specific fields are in use today. For all these tasks, a major step is document segmentation into text lines. Because of the low quality and the complexity of these documents (background noise, artifacts due to aging, interfering lines), automatic text line segmentation remains an open research field. The objective of this paper is to present a survey of existing methods, developed during the last decade and dedicated to documents of historical interest.

Connected through hyperedge HE-1 (induced by source paper "a two stage method for text line detection in historical documents"):
  with members:
    - "baseline detection in historical documents using convolutional u nets"
    - "dhsegment a generic deep learning approach for document segmentation"
    - "multi task handwritten document layout analysis"
    - "pagenet page boundary extraction in historical handwritten documents"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "full page text recognition learning where to start and when to stop"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "regular expressions for decoding of neural network outputs"

Connected through hyperedge HE-2 (induced by source paper "deep visual template free form parsing"):
  with members:
    - "a two stage method for text line detection in historical documents"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "fast and robust detection of solar modules in electroluminescence images"):
  with members:
    - "automatic classification of defective photovoltaic module cells in electroluminescence images"
    - "segmentation of photovoltaic module cells in electroluminescence images"
    - "you only look once unified real time object detection"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "baseline detection in historical documents using convolutional u nets"):
  with members:
    - "dilated residual networks"
    - "read bad a new dataset and evaluation scheme for baseline detection in archival documents"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "deep residual learning for image recognition"
    - "multi dimensional recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "towards knowledge discovery from the vatican secret archives in codice ratio episode 1 machine transcription of the manuscripts"):
  with members:
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"

Connected through hyperedge HE-6 (induced by source paper "arabic character segmentation using projection based approach with profile s amplitude filter"):
  with members:
    - "off line arabic handwriting character recognition using word segmentation"
    - "devnagari document segmentation using histogram approach"

Connected through hyperedge HE-7 (induced by source paper "a bottom up procedure for text line segmentation of latin script"):
  with members:
    - "extraction of line word character segments directly from run length compressed printed text documents"

Connected through hyperedge HE-8 (induced by source paper "joint line segmentation and transcription for end to end handwritten paragraph recognition"):
  with members:
    - "recursive recurrent nets with attention modeling for ocr in the wild"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"
    - "recurrent spatial transformer networks"
    - "listen attend and spell"
    - "spatial transformer networks"
    - "show attend and tell neural image caption generation with visual attention"
    - "densecap fully convolutional localization networks for dense captioning"
    - "draw a recurrent neural network for image generation"

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)