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: "look more than once an accurate detector for text of arbitrary shapes"
  Abstract: Previous scene text detection methods have progressed substantially over the past years. However, limited by the receptive field of CNNs and the simple representations like rectangle bounding box or quadrangle adopted to describe text, previous methods may fall short when dealing with more challenging text instances, such as extremely long text and arbitrarily shaped text. To address these two problems, we present a novel text detector namely LOMO, which localizes the text progressively for multiple times (or in other word, LOok More than Once). LOMO consists of a direct regressor (DR), an iterative refinement module (IRM) and a shape expression module (SEM). At first, text proposals in the form of quadrangle are generated by DR branch. Next, IRM progressively perceives the entire long text by iterative refinement based on the extracted feature blocks of preliminary proposals. Finally, a SEM is introduced to reconstruct more precise representation of irregular text by considering the geometry properties of text instance, including text region, text center line and border offsets. The state-of-the-art results on several public benchmarks including ICDAR2017-RCTW, SCUT-CTW1500, Total-Text, ICDAR2015 and ICDAR17-MLT confirm the striking robustness and effectiveness of LOMO.

Connected through hyperedge HE-1 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "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"
    - "deep visual template free form parsing"
    - "msr multi scale shape regression for scene text detection"
    - "rethinking table recognition using graph neural networks"
    - "towards robust curve text detection with conditional spatial expansion"

Connected through hyperedge HE-2 (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"
    - "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"
    - "efficient and accurate arbitrary shaped text detection with pixel aggregation network"

Connected through hyperedge HE-3 (induced by source paper "puzzlenet scene text detection by segment context graph learning"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "convolutional character networks"
    - "character region awareness for text detection"
    - "shape robust text detection with progressive scale expansion network"
    - "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"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"

Connected through hyperedge HE-4 (induced by source paper "deep relational reasoning graph network for arbitrary shape text detection"):
  with members:
    - "arbitrary shape scene text detection with adaptive text region representation"
    - "textfield learning a deep direction field for irregular scene text detection"
    - "character region awareness for text detection"
    - "linkage based face clustering via graph convolution network"
    - "msr multi scale shape regression for scene text detection"
    - "efficient and accurate arbitrary shaped text detection with pixel aggregation network"
    - "towards robust curve text detection with conditional spatial expansion"
    - "shape robust text detection with progressive scale expansion network"

Connected through hyperedge HE-5 (induced by source paper "text perceptron towards end to end arbitrary shaped text spotting"):
  with members:
    - "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"
    - "accurate scene text detection through border semantics awareness and bootstrapping"

Connected through hyperedge HE-6 (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"
    - "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"
    - "pyramid mask text detector"

Connected through hyperedge HE-7 (induced by source paper "a single shot arbitrarily shaped text detector based on context attended multi task learning"):
  with members:
    - "textfield learning a deep direction field for irregular scene text detection"
    - "inceptext a new inception text module with deformable psroi pooling for multi oriented scene text detection"
    - "rotation sensitive regression for oriented scene text detection"
    - "arbitrary oriented scene text detection via rotation proposals"
    - "learning a discriminative feature network for semantic segmentation"
    - "textsnake a flexible representation for detecting text of arbitrary shapes"
    - "affinity derivation and graph merge for instance segmentation"
    - "pixellink detecting scene text via instance segmentation"

Connected through hyperedge HE-8 (induced by source paper "learning modulated loss for rotated object detection"):
  with members:
    - "object detection in optical remote sensing images a survey and a new benchmark"
    - "reppoints point set representation for object detection"
    - "omnidirectional scene text detection with sequential free box discretization"
    - "foveabox beyond anchor based object detector"
    - "r3det refined single stage detector with feature refinement for rotating object"
    - "hybrid task cascade for instance segmentation"
    - "fcos fully convolutional one stage object detection"
    - "arbitrary oriented scene text detection via rotation proposals"

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)