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: "end to end interpretation of the french street name signs dataset"
  Abstract: We introduce the French Street Name Signs (FSNS) Dataset consisting of more than a million images of street name signs cropped from Google Street View images of France. Each image contains several views of the same street name sign. Every image has normalized, title case folded ground-truth text as it would appear on a map. We believe that the FSNS dataset is large and complex enough to train a deep network of significant complexity to solve the street name extraction problem "end-to-end" or to explore the design trade-offs between a single complex engineered network and multiple sub-networks designed and trained to solve sub-problems. We present such an "end-to-end" network/graph for Tensor Flow and its results on the FSNS dataset.

Connected through hyperedge HE-1 (induced by source paper "scene text recognition via transformer"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "icdar2019 robust reading challenge on arbitrary shaped text rrc art"
    - "2d attentional irregular scene text recognizer"
    - "a simple and robust convolutional attention network for irregular text recognition"
    - "scene text recognition from two dimensional perspective"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "edit probability for scene text recognition"
    - "show attend and read a simple and strong baseline for irregular text recognition"

Connected through hyperedge HE-2 (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"
    - "unconstrained scene text and video text recognition for arabic script"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "a fine grained approach to scene text script identification"
    - "synthetic data for text localisation in natural images"

Connected through hyperedge HE-3 (induced by source paper "fooling ocr systems with adversarial text images"):
  with members:
    - "audio adversarial examples targeted attacks on speech to text"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "towards crafting text adversarial samples"
    - "adversarial attacks on neural network policies"
    - "robust physical world attacks on deep learning models"
    - "dolphinattack inaudible voice commands"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "synthetic and natural noise both break neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "large scale scene text verification with guided attention"):
  with members:
    - "east an efficient and accurate scene text detector"
    - "attention based extraction of structured information from street view imagery"
    - "detecting oriented text in natural images by linking segments"
    - "multi oriented text detection with fully convolutional networks"
    - "detecting text in natural image with connectionist text proposal network"
    - "robust scene text recognition with automatic rectification"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "vqa visual question answering"

Connected through hyperedge HE-5 (induced by source paper "advances of scene text datasets"):
  with members:
    - "synthetic data for text localisation in natural images"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-6 (induced by source paper "attention based extraction of structured information from street view imagery"):
  with members:
    - "an analysis of deep neural network models for practical applications"
    - "text attentional convolutional neural network for scene text detection"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "recursive recurrent nets with attention modeling for ocr in the wild"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"
    - "reward augmented maximum likelihood for neural structured prediction"
    - "joint line segmentation and transcription for end to end handwritten paragraph recognition"
    - "coco text dataset and benchmark for text detection and recognition in natural images"

Connected through hyperedge HE-7 (induced by source paper "wetext scene text detection under weak supervision"):
  with members:
    - "wordfence text detection in natural images with border awareness"
    - "text flow a unified text detection system in natural scene images"
    - "text attentional convolutional neural network for scene text detection"
    - "multi oriented text detection with fully convolutional networks"
    - "ssd single shot multibox detector"
    - "accurate text localization in natural image with cascaded convolutional text network"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural network"

Connected through hyperedge HE-8 (induced by source paper "see towards semi supervised end to end scene text recognition"):
  with members:
    - "visualbackprop efficient visualization of cnns"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "attention based extraction of structured information from street view imagery"
    - "synthetic data for text localisation in natural images"
    - "recurrent spatial transformer networks"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "instance aware semantic segmentation via multi task network cascades"
    - "spatial transformer 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)