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 text recognition with hybrid hmm maxout models"
  Abstract: The problem of detecting and recognizing text in natural scenes has proved to be more challenging than its counterpart in documents, with most of the previous work focusing on a single part of the problem. In this work, we propose new solutions to the character and word recognition problems and then show how to combine these solutions in an end-to-end text-recognition system. We do so by leveraging the recently introduced Maxout networks along with hybrid HMM models that have proven useful for voice recognition. Using these elements, we build a tunable and highly accurate recognition system that beats state-of-the-art results on all the sub-problems for both the ICDAR 2003 and SVT benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "scene text synthesis for efficient and effective deep network training"):
  with members:
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "spatial fusion gan for image synthesis"
    - "esir end to end scene text recognition via iterative image rectification"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "cycada cycle consistent adversarial domain adaptation"
    - "single shot text detector with regional attention"

Connected through hyperedge HE-2 (induced by source paper "2d ctc for scene text recognition"):
  with members:
    - "scene text recognition from two dimensional perspective"
    - "an end to end textspotter with explicit alignment and attention"
    - "edit probability for scene text recognition"
    - "scan sliding convolutional attention network for scene text recognition"
    - "esir end to end scene text recognition via iterative image rectification"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "visual attention models for scene text recognition"
    - "aon towards arbitrarily oriented text recognition"

Connected through hyperedge HE-3 (induced by source paper "learning to read by spelling towards unsupervised text recognition"):
  with members:
    - "unsupervised cipher cracking using discrete gans"
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised sequence classification using sequential output statistics"
    - "unsupervised neural machine translation"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "precomputed real time texture synthesis with markovian generative adversarial networks"
    - "recursive recurrent nets with attention modeling for ocr in the wild"

Connected through hyperedge HE-4 (induced by source paper "inductive visual localisation factorised training for superior generalisation"):
  with members:
    - "making neural programming architectures generalize via recursion"
    - "single shot text detector with regional attention"
    - "dota a large scale dataset for object detection in aerial images"
    - "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras"
    - "dilated residual networks"
    - "identity mappings in deep residual networks"
    - "recursive recurrent nets with attention modeling for ocr in the wild"
    - "synthetic data for text localisation in natural images"

Connected through hyperedge HE-5 (induced by source paper "nrtr a no recurrence sequence to sequence model 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"
    - "attention is all you need"
    - "visual attention models for scene text recognition"
    - "aon towards arbitrarily oriented text recognition"
    - "reading scene text with attention convolutional sequence modeling"
    - "scene text recognition with sliding convolutional character models"

Connected through hyperedge HE-6 (induced by source paper "textboxes a single shot oriented scene text detector"):
  with members:
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "detecting oriented text in natural images by linking segments"
    - "text attentional convolutional neural network for scene text detection"
    - "ssd single shot multibox detector"
    - "scene text detection via holistic multi channel prediction"
    - "pvanet deep but lightweight neural networks for real time object detection"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural network"

Connected through hyperedge HE-7 (induced by source paper "edit probability for scene text recognition"):
  with members:
    - "focusing attention towards accurate text recognition in natural images"
    - "recursive recurrent nets with attention modeling for ocr in the wild"
    - "joint ctc attention based end to end speech recognition using multi task learning"
    - "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"
    - "end to end attention based large vocabulary speech recognition"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "double supervised network with attention mechanism for scene text recognition"):
  with members:
    - "stn ocr a single neural network for text detection and text recognition"
    - "aon towards arbitrarily oriented text recognition"
    - "focusing attention towards accurate text recognition in natural images"
    - "recursive recurrent nets with attention modeling for ocr in the wild"
    - "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"
    - "reading scene text in deep convolutional sequences"

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)