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: "reading scene text with attention convolutional sequence modeling"
  Abstract: Reading text in the wild is a challenging task in the field of computer vision. Existing approaches mainly adopted Connectionist Temporal Classification (CTC) or Attention models based on Recurrent Neural Network (RNN), which is computationally expensive and hard to train. In this paper, we present an end-to-end Attention Convolutional Network for scene text recognition. Firstly, instead of RNN, we adopt the stacked convolutional layers to effectively capture the contextual dependencies of the input sequence, which is characterized by lower computational complexity and easier parallel computation. Compared to the chain structure of recurrent networks, the Convolutional Neural Network (CNN) provides a natural way to capture long-term dependencies between elements, which is 9 times faster than Bidirectional Long Short-Term Memory (BLSTM). Furthermore, in order to enhance the representation of foreground text and suppress the background noise, we incorporate the residual attention modules into a small densely connected network to improve the discriminability of CNN features. We validate the performance of our approach on the standard benchmarks, including the Street View Text, IIIT5K and ICDAR datasets. As a result, state-of-the-art or highly-competitive performance and efficiency show the superiority of the proposed approach.

Connected through hyperedge HE-1 (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"
    - "deformable convolutional networks"
    - "focusing attention towards accurate text recognition in natural images"

Connected through hyperedge HE-2 (induced by source paper "fast dense residual network enhancing global dense feature flow for text recognition"):
  with members:
    - "fast densenet towards efficient and accurate text recognition with fast dense networks"
    - "fully convolutional intensive feature flow neural network for text recognition"
    - "denoising prior driven deep neural network for image restoration"
    - "esir end to end scene text recognition via iterative image rectification"
    - "residual dense network for image super resolution"
    - "aon towards arbitrarily oriented text recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep forest towards an alternative to deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "scene text visual question answering"):
  with members:
    - "towards vqa models that can read"
    - "icdar 2019 competition on scene text visual question answering"
    - "single shot scene text retrieval"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "tallyqa answering complex counting questions"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-4 (induced by source paper "a simple and robust convolutional attention network for irregular text recognition"):
  with members:
    - "universal transformers"
    - "single shot scene text retrieval"
    - "scene text recognition from two dimensional perspective"
    - "nrtr a no recurrence sequence to sequence model for scene text recognition"
    - "edit probability for scene text recognition"
    - "scan sliding convolutional attention network for scene text recognition"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "show attend and read a simple and strong baseline for irregular text recognition"

Connected through hyperedge HE-5 (induced by source paper "fully convolutional intensive feature flow neural network for text recognition"):
  with members:
    - "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames"
    - "scene text recognition from two dimensional perspective"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "stn ocr a single neural network for text detection and text recognition"
    - "aon towards arbitrarily oriented text recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-6 (induced by source paper "fast densenet towards efficient and accurate text recognition with fast dense networks"):
  with members:
    - "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "scene text recognition from two dimensional perspective"
    - "densereg fully convolutional dense shape regression in the wild"
    - "residual dense network for image super resolution"
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "aon towards arbitrarily oriented text recognition"
    - "deep forest towards an alternative to deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "bidirectional scene text recognition with a single decoder"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "symmetry constrained rectification network for scene text recognition"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "nrtr a no recurrence sequence to sequence model for scene text recognition"
    - "esir end to end scene text recognition via iterative image rectification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"

Connected through hyperedge HE-8 (induced by source paper "improving long handwritten text line recognition with convolutional multi way associative memory"):
  with members:
    - "learning to remember more with less memorization"
    - "dual control memory augmented neural networks for treatment recommendations"
    - "memory augmented neural networks for predictive process analytics"
    - "robust and scalable differentiable neural computer for question answering"
    - "rosetta large scale system for text detection and recognition in images"
    - "dual memory neural computer for asynchronous two view sequential learning"
    - "handwritten digit string recognition by combination of residual network and rnn ctc"
    - "implicit language model in lstm for ocr"

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)