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: "attention based extraction of structured information from street view imagery"
  Abstract: We present a neural network model - based on CNNs, RNNs and a novel attention mechanism - which achieves 84.2% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith'16), which achieved 72.46%. Furthermore, our new method is much simpler and more general than the previous approach. To demonstrate the generality of our model, we show that it also performs well on an even more challenging dataset derived from Google Street View, in which the goal is to extract business names from store fronts. Finally, we study the speed/accuracy tradeoff that results from using CNN feature extractors of different depths. Surprisingly, we find that deeper is not always better (in terms of accuracy, as well as speed). Our resulting model is simple, accurate and fast, allowing it to be used at scale on a variety of challenging real-world text extraction problems.

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 "faclstm convlstm with focused attention for scene text recognition"):
  with members:
    - "scene text recognition from two dimensional perspective"
    - "edit probability for scene text recognition"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "stn ocr a single neural network for text detection and text recognition"
    - "aon towards arbitrarily oriented text recognition"
    - "reading scene text with attention convolutional sequence modeling"
    - "deformable convolutional networks"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-3 (induced by source paper "towards unconstrained end to end text spotting"):
  with members:
    - "textfield learning a deep direction field for irregular scene text detection"
    - "msr multi scale shape regression for scene text detection"
    - "rotation sensitive regression for oriented scene text detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "e2e mlt an unconstrained end to end method for multi language scene text"

Connected through hyperedge HE-4 (induced by source paper "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"):
  with members:
    - "image transformer"
    - "learning deep disentangled embeddings with the f statistic loss"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "isolating sources of disentanglement in variational autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deep learning a critical appraisal"
    - "towards a definition of disentangled representations"

Connected through hyperedge HE-5 (induced by source paper "eaten entity aware attention for single shot visual text extraction"):
  with members:
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "single shot scene text retrieval"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "a novel integrated framework for learning both text detection and recognition"
    - "textnet irregular text reading from images with an end to end trainable network"
    - "wordsup exploiting word annotations for character based text detection"
    - "east an efficient and accurate scene text detector"

Connected through hyperedge HE-6 (induced by source paper "autonomous removal of perspective distortion for robotic elevator button recognition"):
  with members:
    - "learning to interrupt a hierarchical deep reinforcement learning framework for efficient exploration"
    - "ssd single shot multibox detector"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"):
  with members:
    - "rotation sensitive regression for oriented scene text detection"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "edit probability for scene text recognition"
    - "e2e mlt an unconstrained end to end method for multi language scene text"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-8 (induced by source paper "accurate data efficient unconstrained text recognition with convolutional neural networks"):
  with members:
    - "an efficient end to end neural model for handwritten text recognition"
    - "scene text recognition from two dimensional perspective"
    - "handwriting recognition of historical documents with few labeled data"
    - "autoaugment learning augmentation policies from data"
    - "attention u net learning where to look for the pancreas"
    - "how many labeled license plates are needed"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

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)