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: "towards end to end text spotting with convolutional recurrent neural networks"
  Abstract: In this work, we jointly address the problem of text detection and recognition in natural scene images based on convolutional recurrent neural networks. We propose a unified network that simultaneously localizes and recognizes text with a single forward pass, avoiding intermediate processes like image cropping and feature re-calculation, word separation, or character grouping. In contrast to existing approaches that consider text detection and recognition as two distinct tasks and tackle them one by one, the proposed framework settles these two tasks concurrently. The whole framework can be trained end-to-end, requiring only images, the ground-truth bounding boxes and text labels. Through end-to-end training, the learned features can be more informative, which improves the overall performance. The convolutional features are calculated only once and shared by both detection and recognition, which saves processing time. Our proposed method has achieved competitive performance on several benchmark datasets.

Connected through hyperedge HE-1 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "convolutional character networks"
    - "character region awareness for text detection"
    - "towards end to end text spotting in natural scenes"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-2 (induced by source paper "text perceptron towards end to end arbitrary shaped text spotting"):
  with members:
    - "look more than once an accurate detector for text of arbitrary shapes"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "towards robust curve text detection with conditional spatial expansion"
    - "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"
    - "an end to end textspotter with explicit alignment and attention"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"

Connected through hyperedge HE-3 (induced by source paper "a multi oriented chinese keyword spotter guided by text line detection"):
  with members:
    - "textboxes a single shot oriented scene text detector"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "neural ctrl f segmentation free query by string word spotting in handwritten manuscript collections"
    - "synthetic data for text localisation in natural images"
    - "textboxes a fast text detector with a single deep neural network"
    - "query by string word spotting based on character bi gram indexing"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "reading text in the wild with convolutional neural networks"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "chinese street view text large scale chinese text reading with partially supervised learning"):
  with members:
    - "rotation sensitive regression for oriented scene text detection"
    - "chinese text in the wild"
    - "fots fast oriented text spotting with a unified network"
    - "an end to end textspotter with explicit alignment and attention"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "textboxes a single shot oriented scene text detector"
    - "exploring the limits of weakly supervised pretraining"
    - "textnet irregular text reading from images with an end to end trainable network"

Connected through hyperedge HE-6 (induced by source paper "convolutional character networks"):
  with members:
    - "textfield learning a deep direction field for irregular scene text detection"
    - "cornernet detecting objects as paired keypoints"
    - "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"
    - "textnet irregular text reading from images with an end to end trainable network"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"

Connected through hyperedge HE-7 (induced by source paper "detecting text in the wild with deep character embedding network"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "pixellink detecting scene text via instance segmentation"
    - "learning markov clustering networks for scene text detection"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "wordsup exploiting word annotations for character based text detection"
    - "total text a comprehensive dataset for scene text detection and recognition"
    - "east an efficient and accurate scene text detector"

Connected through hyperedge HE-8 (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"

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)