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: "generating synthetic data for text recognition"
  Abstract: Generating synthetic images is an art which emulates the natural process of image generation in a closest possible manner. In this work, we exploit such a framework for data generation in handwritten domain. We render synthetic data using open source fonts and incorporate data augmentation schemes. As part of this work, we release 9M synthetic handwritten word image corpus which could be useful for training deep network architectures and advancing the performance in handwritten word spotting and recognition tasks.

Connected through hyperedge HE-1 (induced by source paper "ganwriting content conditioned generation of styled handwritten word images"):
  with members:
    - "adversarial generation of handwritten text images conditioned on sequences"
    - "few shot unsupervised image to image translation"
    - "unsupervised writer adaptation for synthetic to real handwritten word recognition"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "generating handwritten chinese characters using cyclegan"

Connected through hyperedge HE-2 (induced by source paper "candidate fusion integrating language modelling into a sequence to sequence handwritten word recognition architecture"):
  with members:
    - "are 2d lstm really dead for offline text recognition"
    - "confidence prediction for lexicon free ocr"
    - "attention is all you need"
    - "carla an open urban driving simulator"
    - "a critical review of recurrent neural networks for sequence learning"
    - "end to end attention based large vocabulary speech recognition"
    - "attention based models for speech recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-3 (induced by source paper "unsupervised writer adaptation for synthetic to real handwritten word recognition"):
  with members:
    - "evaluating sequence to sequence models for handwritten text recognition"
    - "handwriting recognition in low resource scripts using adversarial learning"
    - "hwnet v2 an efficient word image representation for handwritten documents"
    - "boosting handwriting text recognition in small databases with transfer learning"
    - "adversarial discriminative domain adaptation"
    - "zero shot deep domain adaptation"
    - "learning deep representations for word spotting under weak supervision"
    - "scan attend and read end to end handwritten paragraph recognition with mdlstm attention"

Connected through hyperedge HE-4 (induced by source paper "field typing for improved recognition on heterogeneous handwritten forms"):
  with members:
    - "implicit language model in lstm for ocr"
    - "full page text recognition learning where to start and when to stop"
    - "detecting oriented text in natural images by linking segments"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "adam a method for stochastic optimization"
    - "synthetic data and artificial neural networks for natural scene text recognition"
    - "deep structured output learning for unconstrained text recognition"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (induced by source paper "unconstrained scene text and video text recognition for arabic script"):
  with members:
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "reading text in the wild with convolutional neural networks"
    - "supervised mid level features for word image representation"
    - "speech recognition with deep recurrent neural networks"
    - "arabic text recognition in video sequences"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)