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: "full page text recognition learning where to start and when to stop"
  Abstract: Text line detection and localization is a crucial step for full page document analysis, but still suffers from heterogeneity of real life documents. In this paper, we present a new approach for full page text recognition. Localization of the text lines is based on regressions with Fully Convolutional Neural Networks and Multidimensional Long Short-Term Memory as contextual layers. In order to increase the efficiency of this localization method, only the position of the left side of the text lines are predicted. The text recognizer is then in charge of predicting the end of the text to recognize. This method has shown good results for full page text recognition on the highly heterogeneous Maurdor dataset.

Connected through hyperedge HE-1 (induced by source paper "a two stage method for text line detection in historical documents"):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    - "baseline detection in historical documents using convolutional u nets"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"
    - "phocnet a deep convolutional neural network for word spotting in handwritten documents"
    - "regular expressions for decoding of neural network outputs"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"

Connected through hyperedge HE-2 (induced by source paper "field typing for improved recognition on heterogeneous handwritten forms"):
  with members:
    - "implicit language model in lstm for ocr"
    - "detecting oriented text in natural images by linking segments"
    - "generating synthetic data for text recognition"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "deep structured output learning for unconstrained text recognition"

Connected through hyperedge HE-3 (induced by source paper "a human inspired recognition system for premodern japanese historical documents"):
  with members:
    - "multi scale attention with dense encoder for handwritten mathematical expression recognition"
    - "building efficient cnn architecture for offline handwritten chinese character recognition"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural machine translation by jointly learning to align and translate"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "handwriting recognition of historical documents with few labeled data"):
  with members:
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"
    - "recent advances in convolutional neural networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "adadelta an adaptive learning rate method"

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)