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: "automatic script identification in the wild"
  Abstract: With the rapid increase of transnational communication and cooperation, people frequently encounter multilingual scenarios in various situations. In this paper, we are concerned with a relatively new problem: script identification at word or line levels in natural scenes. A large-scale dataset with a great quantity of natural images and 10 types of widely used languages is constructed and released. In allusion to the challenges in script identification in real-world scenarios, a deep learning based algorithm is proposed. The experiments on the proposed dataset demonstrate that our algorithm achieves superior performance, compared with conventional image classification methods, such as the original CNN architecture and LLC.

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"
    - "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"

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"
    - "a fine grained approach to scene text script identification"
    - "understanding deep neural networks with rectified linear units"

Connected through hyperedge HE-3 (induced by source paper "sequence to label script identification for multilingual ocr"):
  with members:
    - "tensorflow a system for large scale machine learning"
    - "going deeper with convolutions"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-4 (induced by source paper "improving patch based scene text script identification with ensembles of conjoined networks"):
  with members:
    - "a fine grained approach to scene text script identification"
    - "sparse radial sampling lbp for writer identification"
    - "a fast hierarchical method for multi script and arbitrary oriented scene text extraction"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "a fine grained approach to scene text script identification"):
  with members:
    - "sparse radial sampling lbp for writer identification"
    - "a fast hierarchical method for multi script and arbitrary oriented scene text extraction"

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)