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: "see towards semi supervised end to end scene text recognition"
  Abstract: Detecting and recognizing text in natural scene images is a challenging, yet not completely solved task. In recent years several new systems that try to solve at least one of the two sub-tasks (text detection and text recognition) have been proposed. In this paper we present SEE, a step towards semi-supervised neural networks for scene text detection and recognition, that can be optimized end-to-end. Most existing works consist of multiple deep neural networks and several pre-processing steps. In contrast to this, we propose to use a single deep neural network, that learns to detect and recognize text from natural images, in a semi-supervised way. SEE is a network that integrates and jointly learns a spatial transformer network, which can learn to detect text regions in an image, and a text recognition network that takes the identified text regions and recognizes their textual content. We introduce the idea behind our novel approach and show its feasibility, by performing a range of experiments on standard benchmark datasets, where we achieve competitive results.

Connected through hyperedge HE-1 (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-2 (induced by source paper "kiss keeping it simple for scene text recognition"):
  with members:
    - "faclstm convlstm with focused attention for scene text recognition"
    - "on the variance of the adaptive learning rate and beyond"
    - "a simple and robust convolutional attention network for irregular text recognition"
    - "scene text recognition from two dimensional perspective"
    - "group normalization"
    - "edit probability for scene text recognition"
    - "show attend and read a simple and strong baseline for irregular text recognition"
    - "esir end to end scene text recognition via iterative image rectification"

Connected through hyperedge HE-3 (induced by source paper "curved text detection in natural scene images with semi and weakly supervised learning"):
  with members:
    - "min entropy latent model for weakly supervised object detection"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "scene text detection and recognition the deep learning era"
    - "mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes"
    - "towards end to end text spotting with convolutional recurrent neural networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "esir end to end scene text recognition via iterative image rectification"):
  with members:
    - "scene text synthesis for efficient and effective deep network training"
    - "towards robust curve text detection with conditional spatial expansion"
    - "an end to end textspotter with explicit alignment and attention"
    - "verisimilar image synthesis for accurate detection and recognition of texts in scenes"
    - "accurate scene text detection through border semantics awareness and bootstrapping"
    - "edit probability for scene text recognition"
    - "spatial fusion gan for image synthesis"
    - "learning markov clustering networks for scene text detection"

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)