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 fine grained approach to scene text script identification"
  Abstract: This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. #R##N#We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. #R##N#In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online.

Connected through hyperedge HE-1 (induced by source paper "patch aggregator for scene text script identification"):
  with members:
    - "script identification in natural scene image and video frames using an attention based convolutional lstm network"
    - "e2e mlt an unconstrained end to end method for multi language scene text"
    - "sequence to label script identification for multilingual ocr"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "script identification in natural scene image and video frames using an attention based convolutional lstm network"):
  with members:
    - "handwriting trajectory recovery using end to end deep encoder decoder network"
    - "e2e mlt an unconstrained end to end method for multi language scene text"
    - "staff line removal using generative adversarial networks"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "image captioning with semantic attention"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "understanding deep neural networks with rectified linear units"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"

Connected through hyperedge HE-3 (induced by source paper "icdar2019 robust reading challenge on multi lingual scene text detection and recognition rrc mlt 2019"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "character region awareness for text detection"
    - "pyramid mask text detector"
    - "e2e mlt an unconstrained end to end method for multi language scene text"
    - "end to end interpretation of the french street name signs dataset"
    - "unconstrained scene text and video text recognition for arabic script"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "synthetic data for text localisation in natural images"

Connected through hyperedge HE-4 (induced by source paper "deep learning based isolated arabic scene character recognition"):
  with members:
    - "memory matters convolutional recurrent neural network for scene text recognition"
    - "a cnn based scene chinese text recognition algorithm with synthetic data engine"
    - "efficient scene text localization and recognition with local character refinement"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "arabic text recognition in video sequences"

Connected through hyperedge HE-5 (induced by source paper "improving patch based scene text script identification with ensembles of conjoined networks"):
  with members:
    - "sparse radial sampling lbp for writer identification"
    - "automatic script identification in the wild"
    - "a fast hierarchical method for multi script and arbitrary oriented scene text extraction"
    - "caffe convolutional architecture for fast feature embedding"

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)