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 "disentangling monocular 3d object detection"):
  Abstract: In this paper we propose an approach for monocular 3D object detection from a single RGB image, which leverages a novel disentangling transformation for 2D and 3D detection losses and a novel, self-supervised confidence score for 3D bounding boxes. Our proposed loss disentanglement has the twofold advantage of simplifying the training dynamics in the presence of losses with complex interactions of parameters, and sidestepping the issue of balancing independent regression terms. Our solution overcomes these issues by isolating the contribution made by groups of parameters to a given loss, without changing its nature. We further apply loss disentanglement to another novel, signed Intersection-over-Union criterion-driven loss for improving 2D detection results. Besides our methodological innovations, we critically review the AP metric used in KITTI3D, which emerged as the most important dataset for comparing 3D detection results. We identify and resolve a flaw in the 11-point interpolated AP metric, affecting all previously published detection results and particularly biases the results of monocular 3D detection. We provide extensive experimental evaluations and ablation studies on the KITTI3D and nuScenes datasets, setting new state-of-the-art results on object category car by large margins.

Includes members:
  Member "stereo r cnn based 3d object detection for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (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-4 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "nuscenes a multimodal dataset for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review deep learning on 3d point clouds")
  Member "monocular 3d object detection via geometric reasoning on keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  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 "deep learning for generic object detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)