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: "deeptext a unified framework for text proposal generation and text detection in natural images"
  Abstract: In this paper, we develop a novel unified framework called DeepText for text region proposal generation and text detection in natural images via a fully convolutional neural network (CNN). First, we propose the inception region proposal network (Inception-RPN) and design a set of text characteristic prior bounding boxes to achieve high word recall with only hundred level candidate proposals. Next, we present a powerful textdetection network that embeds ambiguous text category (ATC) information and multilevel region-of-interest pooling (MLRP) for text and non-text classification and accurate localization. Finally, we apply an iterative bounding box voting scheme to pursue high recall in a complementary manner and introduce a filtering algorithm to retain the most suitable bounding box, while removing redundant inner and outer boxes for each text instance. Our approach achieves an F-measure of 0.83 and 0.85 on the ICDAR 2011 and 2013 robust text detection benchmarks, outperforming previous state-of-the-art results.

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 "exploring the capacity of sequential free box discretization network for omnidirectional scene text detection"):
  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"
    - "character region awareness for text detection"
    - "region proposal by guided anchoring"
    - "msr multi scale shape regression for scene text detection"
    - "mask scoring r cnn"

Connected through hyperedge HE-3 (induced by source paper "detecting multi oriented text with corner based region proposals"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "deep extreme cut from extreme points to object segmentation"
    - "east an efficient and accurate scene text detector"
    - "oriented response networks"
    - "extreme clicking for efficient object annotation"
    - "single shot text detector with regional attention"
    - "fused text segmentation networks for multi oriented scene text detection"

Connected through hyperedge HE-4 (induced by source paper "stela a real time scene text detector with learned anchor"):
  with members:
    - "region proposal by guided anchoring"
    - "consistent optimization for single shot object detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "fots fast oriented text spotting with a unified network"
    - "scene text detection with supervised pyramid context network"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "geometry normalization networks for accurate scene text detection"):
  with members:
    - "shape robust text detection with progressive scale expansion network"
    - "rotation sensitive regression for oriented scene text detection"
    - "fots fast oriented text spotting with a unified network"
    - "sniper efficient multi scale training"
    - "pixel anchor a fast oriented scene text detector with combined networks"
    - "boosting up scene text detectors with guided cnn"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "deep matching prior network toward tighter multi oriented text detection"

Connected through hyperedge HE-6 (induced by source paper "rfbtd rfb text detector"):
  with members:
    - "receptive field block net for accurate and fast object detection"
    - "text flow a unified text detection system in natural scene images"
    - "multi oriented text detection with fully convolutional networks"
    - "scene text detection via holistic multi channel prediction"
    - "unitbox an advanced object detection network"
    - "detecting text in natural image with connectionist text proposal network"
    - "an end to end trainable neural network for image based sequence recognition and its application to scene text recognition"
    - "u net convolutional networks for biomedical image segmentation"

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"
    - "e2e mlt an unconstrained end to end method for multi language scene text"
    - "textboxes a single shot oriented scene text detector"

Connected through hyperedge HE-8 (induced by source paper "shape robust text detection with progressive scale expansion network"):
  with members:
    - "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"
    - "scene text detection with supervised pyramid context network"
    - "multi oriented scene text detection via corner localization and region segmentation"
    - "wordsup exploiting word annotations for character based text detection"
    - "total text a comprehensive dataset for scene text detection and 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)