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: "incidental scene text understanding recent progresses on icdar 2015 robust reading competition challenge 4"
  Abstract: Different from focused texts present in natural images, which are captured with user's intention and intervention, incidental texts usually exhibit much more diversity, variability and complexity, thus posing significant difficulties and challenges for scene text detection and recognition algorithms. The ICDAR 2015 Robust Reading Competition Challenge 4 was launched to assess the performance of existing scene text detection and recognition methods on incidental texts as well as to stimulate novel ideas and solutions. This report is dedicated to briefly introduce our strategies for this challenging problem and compare them with prior arts in this field.

Connected through hyperedge HE-1 (induced by source paper "generating text sequence images for recognition"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "edit probability for scene text recognition"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "focusing attention towards accurate text recognition in natural images"
    - "recursive recurrent nets with attention modeling for ocr in the wild"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "extracting textual overlays from social media videos using neural networks"):
  with members:
    - "text flow a unified text detection system in natural scene images"
    - "overlay text extraction from tv news broadcast"
    - "ssd single shot multibox detector"
    - "textboxes a fast text detector with a single deep neural network"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "unsupervised extraction of video highlights via robust recurrent auto encoders"
    - "synthetic data and artificial neural networks for natural scene text recognition"

Connected through hyperedge HE-3 (induced by source paper "deep matching prior network toward tighter multi oriented text detection"):
  with members:
    - "multi oriented text detection with fully convolutional networks"
    - "ssd single shot multibox detector"
    - "detecting text in natural image with connectionist text proposal network"
    - "you only look once unified real time object detection"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "object detection networks on convolutional feature maps"
    - "very deep convolutional networks for large scale image recognition"

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)