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 "ienet interacting embranchment one stage anchor free detector for orientation aerial object detection"):
  Abstract: Object detection in aerial images is a challenging task due to its lack of visiable features and variant orientation of objects. Currently, amount of R-CNN framework based detectors have made significant progress in predicting targets by horizontal bounding boxes (HBB) and oriented bounding boxes (OBB). However, there is still open space for one-stage anchor free solutions. This paper proposes a one-stage anchor free detector for orientional object in aerial images, which is built upon a per-pixel prediction fashion detector. We make it possible by developing a branch interacting module with a self-attention mechanism to fuse features from classification and box regression branchs. Moreover a geometric transformation is employed in angle prediction to make it more manageable for the prediction network. We also introduce an IOU loss for OBB detection, which is more efficient than regular polygon IOU. The propsed method is evaluated on DOTA and HRSC2016 datasets, and the outcomes show the higher OBB detection performance from our propsed IENet when compared with the state-of-the-art detectors.

Includes members:
  Member "interlaced sparse self attention for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images")
  Member "omnidirectional scene text detection with sequential free box discretization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "convolutional character networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "abcnet real time scene text spotting with adaptive bezier curve network")
  Member "character region awareness for text detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "fcos fully convolutional one stage object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "objects as points":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "cornernet detecting objects as paired keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "scene text detection with supervised pyramid context network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)