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: "improving patch based scene text script identification with ensembles of conjoined networks"
  Abstract: This paper focuses on the problem of script identification in scene text images. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed aspect ratio as in the typical use of holistic CNN classifiers, we propose here a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. #R##N#We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme. Our experiments with this learning procedure demonstrate state-of-the-art results in two public script identification datasets. #R##N#In addition, we propose a new public benchmark dataset for the evaluation of multi-lingual scene text end-to-end reading systems. Experiments done in this dataset demonstrate the key role of script identification in a complete end-to-end system that combines our script identification method with a previously published text detector and an off-the-shelf OCR engine.

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"
    - "a fine grained approach to scene text script identification"
    - "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"
    - "a fine grained approach to scene text script identification"
    - "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"
    - "a fine grained approach to scene text script identification"
    - "synthetic data for text localisation in natural images"

Connected through hyperedge HE-4 (induced by source paper "indic handwritten script identification using offline online multimodal deep network"):
  with members:
    - "deep learning for word level handwritten indic script identification"
    - "handwriting trajectory recovery using end to end deep encoder decoder network"
    - "cross language framework for word recognition and spotting of indic scripts"
    - "hmm based writer identification in music score documents without staff line removal"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "hmm based indic handwritten word recognition using zone segmentation"
    - "local binary pattern for word spotting in handwritten historical document"
    - "compact bilinear pooling"

Connected through hyperedge HE-5 (induced by source paper "textproposals a text specific selective search algorithm for word spotting in the wild"):
  with members:
    - "a fast hierarchical method for multi script and arbitrary oriented scene text extraction"
    - "reading text in the wild with convolutional neural networks"
    - "robust text detection in natural scene images"
    - "end to end text recognition with hybrid hmm maxout models"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "obtaining calibrated probabilities from boosting"

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)