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 "centermask real time anchor free instance segmentation"):
  Abstract: We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN. Plugged into the FCOS object detector, the SAG-Mask branch predicts a segmentation mask on each box with the spatial attention map that helps to focus on informative pixels and suppress noise. We also present an improved VoVNetV2 with two effective strategies: adds (1) residual connection for alleviating the saturation problem of larger VoVNet and (2) effective Squeeze-Excitation (eSE) deals with the information loss problem of original SE. With SAG-Mask and VoVNetV2, we deign CenterMask and CenterMask-Lite that are targeted to large and small models, respectively. CenterMask outperforms all previous state-of-the-art models at a much faster speed. CenterMask-Lite also achieves 33.4\% mask AP / 38.0\% box AP, outperforming the state-of-the-art by 2.6 / 7.0 AP gain, respectively, at over 35fps on Titan Xp. We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively. Code will be released.

Includes members:
  Member "retinamask learning to predict masks improves state of the art single shot detection for free":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task collaborative network for joint referring expression comprehension and segmentation")
  Member "centernet keypoint triplets for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "revisiting the sibling head in object detector")
  Member "an energy and gpu computation efficient backbone network for real time object detection":
    also connected through other hyperedges:
      (none)
  Member "an empirical study of spatial attention mechanisms in deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attentive group equivariant convolutional networks")
  Member "mask scoring r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "proposal learning for semi supervised object detection")
  Member "high resolution representations for labeling pixels and regions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "farsee net real time semantic segmentation by efficient multi scale context aggregation and feature space super resolution")
  Member "tensormask a foundation for dense object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "fcos fully convolutional one stage object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")

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)