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: "recursive recurrent nets with attention modeling for ocr in the wild"
  Abstract: We present recursive recurrent neural networks with attention modeling (R$^2$AM) for lexicon-free optical character recognition in natural scene images. The primary advantages of the proposed method are: (1) use of recursive convolutional neural networks (CNNs), which allow for parametrically efficient and effective image feature extraction; (2) an implicitly learned character-level language model, embodied in a recurrent neural network which avoids the need to use N-grams; and (3) the use of a soft-attention mechanism, allowing the model to selectively exploit image features in a coordinated way, and allowing for end-to-end training within a standard backpropagation framework. We validate our method with state-of-the-art performance on challenging benchmark datasets: Street View Text, IIIT5k, ICDAR and Synth90k.

Connected through hyperedge HE-1 (induced by source paper "efficient backbone search for scene text recognition"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "efficient neural architecture search via proximal iterations"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "detnas backbone search for object detection"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-2 (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-3 (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"
    - "attention based extraction of structured information from street view imagery"
    - "reading scene text with attention convolutional sequence modeling"
    - "deformable convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "gtc guided training of ctc towards efficient and accurate scene text recognition"):
  with members:
    - "a simple and robust convolutional attention network for irregular text recognition"
    - "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"
    - "aon towards arbitrarily oriented text recognition"
    - "focusing attention towards accurate text recognition in natural images"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "scene text recognition with finer grid rectification"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "esir end to end scene text recognition via iterative image rectification"
    - "aon towards arbitrarily oriented text recognition"
    - "focusing attention towards accurate text recognition in natural images"
    - "synthetic data for text localisation in natural images"
    - "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"
    - "spatial transformer networks"

Connected through hyperedge HE-6 (induced by source paper "ga dan geometry aware domain adaptation network for scene text detection and recognition"):
  with members:
    - "adaptive composition gan towards realistic image synthesis"
    - "scene text synthesis for efficient and effective deep network training"
    - "rotation sensitive regression for oriented scene text detection"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "fots fast oriented text spotting with a unified network"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "pixellink detecting scene text via instance segmentation"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"

Connected through hyperedge HE-7 (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-8 (induced by source paper "towards end to end text spotting in natural scenes"):
  with members:
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "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"
    - "edit probability for scene text recognition"
    - "scene text detection with supervised pyramid context network"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "textboxes a single shot oriented scene text detector"

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)