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: "e2e mlt an unconstrained end to end method for multi language scene text"
  Abstract: An end-to-end method for multi-language scene text localization, recognition and script identification is proposed. The approach is based on a set of convolutional neural nets. The method, called E2E-MLT, achieves state-of-the-art performance for both joint localization and script identification in natural images and in cropped word script identification. E2E-MLT is the first published multi-language OCR for scene text. The experiments show that obtaining accurate multi-language multi-script annotations is a challenging problem.

Connected through hyperedge HE-1 (induced by source paper "fine grained image classification and retrieval by combining visual and locally pooled textual features"):
  with members:
    - "towards vqa models that can read"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "on the variance of the adaptive learning rate and beyond"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "lookahead optimizer k steps forward 1 step back"
    - "single shot scene text retrieval"
    - "an end to end textspotter with explicit alignment and attention"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-2 (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"
    - "scene text detection and recognition the deep learning era"

Connected through hyperedge HE-3 (induced by source paper "look more than once an accurate detector for text of arbitrary shapes"):
  with members:
    - "inceptext a new inception text module with deformable psroi pooling for multi oriented scene text detection"
    - "shape robust text detection with progressive scale expansion network"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "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"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "sliding line point regression for shape robust scene text detection"

Connected through hyperedge HE-4 (induced by source paper "patch aggregator for scene text script identification"):
  with members:
    - "script identification in natural scene image and video frames using an attention based convolutional lstm network"
    - "sequence to label script identification for multilingual ocr"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "a fine grained approach to scene text script identification"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "script identification in natural scene image and video frames using an attention based convolutional lstm network"):
  with members:
    - "handwriting trajectory recovery using end to end deep encoder decoder network"
    - "staff line removal using generative adversarial networks"
    - "exploring spatial context for 3d semantic segmentation of point clouds"
    - "image captioning with semantic attention"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "a fine grained approach to scene text script identification"
    - "understanding deep neural networks with rectified linear units"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"

Connected through hyperedge HE-6 (induced by source paper "icdar2019 robust reading challenge on multi lingual scene text detection and recognition rrc mlt 2019"):
  with members:
    - "what is wrong with scene text recognition model comparisons dataset and model analysis"
    - "character region awareness for text detection"
    - "pyramid mask text detector"
    - "end to end interpretation of the french street name signs dataset"
    - "unconstrained scene text and video text recognition for arabic script"
    - "improving patch based scene text script identification with ensembles of conjoined networks"
    - "a fine grained approach to scene text script identification"
    - "synthetic data for text localisation in natural images"

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"
    - "textboxes a single shot oriented scene text detector"
    - "multi oriented scene text detection via corner localization and region 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)