human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks"):
  Abstract: We introduce a new arbitrary-shaped text detection approach named ReLaText by formulating text detection as a visual relationship detection problem. To demonstrate the effectiveness of this new formulation, we start from using a "link" relationship to address the challenging text-line grouping problem firstly. The key idea is to decompose text detection into two subproblems, namely detection of text primitives and prediction of link relationships between nearby text primitive pairs. Specifically, an anchor-free region proposal network based text detector is first used to detect text primitives of different scales from different feature maps of a feature pyramid network, from which a text primitive graph is constructed by linking each pair of nearby text primitives detected from a same feature map with an edge. Then, a Graph Convolutional Network (GCN) based link relationship prediction module is used to prune wrongly-linked edges in the text primitive graph to generate a number of disjoint subgraphs, each representing a detected text instance. As GCN can effectively leverage context information to improve link prediction accuracy, our GCN based text-line grouping approach can achieve better text detection accuracy than previous text-line grouping methods, especially when dealing with text instances with large inter-character or very small inter-line spacings. Consequently, the proposed ReLaText achieves state-of-the-art performance on five public text detection benchmarks, namely RCTW-17, MSRA-TD500, Total-Text, CTW1500 and DAST1500.

Includes members:
  Member "arbitrary shape scene text detection with adaptive text region representation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "look more than once an accurate detector for text of arbitrary shapes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "textfield learning a deep direction field for irregular scene text detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "omnidirectional scene text detection with sequential free box discretization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "arbitrary oriented object detection with circular smooth label")
  Member "character region awareness for text detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "deep visual template free form parsing":
    also connected through other hyperedges:
      (none)
  Member "msr multi scale shape regression for scene text detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep relational reasoning graph network for arbitrary shape text detection")
  Member "rethinking table recognition using graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gfte graph based financial table extraction")

Question: Please tell me which class the source paper 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)