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 "sdod real time segmenting and detecting 3d objects by depth"):
  Abstract: Most existing instance segmentation methods only focus on 2D objects and are not suitable for 3D scenes such as autonomous driving. In this paper, we propose a model that splits instance segmentation and object detection into two parallel branches. We discretize the objects depth into "depth categories" (background set to 0, objects set to [1, K]), then the instance segmentation task has been transformed into a pixel-level classification task. Mask branch predicts pixel-level "depth categories", 3D branch predicts instance-level "depth categories", we produce instance mask by assigning pixels which have same "depth categories" to each instance. In addition, in order to solve the problem of imbalanced between mask labels and 3D labels in the KITTI dataset (200 for mask, 7481 for 3D), we use unreal mask generated by other instance segmentation method to train mask branch. Despite the use of unreal mask labels, experiments result on KITTI dataset still achieves state-of-the-art performance in vehicle instance segmentation.

Includes members:
  Member "sdnet semantically guided depth estimation network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantically guided representation learning for self supervised monocular depth")
  Member "yolact better real time instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "blendmask top down meets bottom up for instance segmentation")
  Member "mask scoring r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "proposal learning for semi supervised object detection")
  Member "fcos fully convolutional one stage object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "solo segmenting objects by locations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "monocular 3d object detection leveraging accurate proposals and shape reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "efficient interactive annotation of segmentation datasets with polygon rnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "deep ordinal regression network for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)