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: "icdar 2015 text reading in the wild competition"
  Abstract: Recently, text detection and recognition in natural scenes are becoming increasing popular in the computer vision community as well as the document analysis community. However, majority of the existing ideas, algorithms and systems are specifically designed for English. This technical report presents the final results of the ICDAR 2015 Text Reading in the Wild (TRW 2015) competition, which aims at establishing a benchmark for assessing detection and recognition algorithms devised for both Chinese and English scripts and providing a playground for researchers from the community. In this article, we describe in detail the dataset, tasks, evaluation protocols and participants of this competition, and report the performance of the participating methods. Moreover, promising directions for future research are discussed.

Connected through hyperedge HE-1 (induced by source paper "arbitext arbitrary oriented text detection in unconstrained scene"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "license plate detection and recognition using deeply learned convolutional neural networks"
    - "east an efficient and accurate scene text detector"
    - "deformable convolutional networks"
    - "deep direct regression for multi oriented scene text detection"
    - "detecting oriented text in natural images by linking segments"
    - "multi oriented text detection with fully convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "scene text recognition with sliding convolutional character models"):
  with members:
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "recursive recurrent nets with attention modeling for ocr in the wild"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "reading scene text in deep convolutional sequences"
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "deep structured output learning for unconstrained text recognition"
    - "reading text in the wild with convolutional neural networks"
    - "supervised mid level features for word image representation"

Connected through hyperedge HE-3 (induced by source paper "a cnn based scene chinese text recognition algorithm with synthetic data engine"):
  with members:
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "multi digit number recognition from street view imagery using deep convolutional neural networks"

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)