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 "single stage monocular 3d object detection with virtual cameras"):
  Abstract: While expensive LiDAR and stereo camera rigs have enabled the development of successful 3D object detection methods, monocular RGB-only approaches still lag significantly behind. Our work advances the state of the art by introducing MoVi-3D, a novel, single-stage deep architecture for monocular 3D object detection. At its core, MoVi-3D leverages geometrical information to generate synthetic views from virtual cameras at both, training and test time, resulting in normalized object appearance with respect to distance. Our synthetically generated views facilitate the detection task as they cut down the variability in visual appearance associated to objects placed at different distances from the camera. As a consequence, the deep model is relieved from learning depth-specific representations and its complexity can be significantly reduced. In particular we show that our proposed concept of exploiting virtual cameras enables us to set new state-of-the-art results on the popular KITTI3D benchmark using just a lightweight, single-stage architecture.

Includes members:
  Member "disentangling monocular 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "pseudo lidar accurate depth for 3d object detection in autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving")
  Member "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "smoke single stage monocular 3d object detection via keypoint estimation")
  Member "deep fitting degree scoring network for monocular 3d object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "gs3d an efficient 3d object detection framework for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving")
  Member "m3d rpn monocular 3d region proposal network for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "monocular 3d object detection leveraging accurate proposals and shape reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")

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)