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 "cubeslam monocular 3d object detection and slam without prior models"):
  Abstract: We present a method for single image 3D cuboid object detection and multi-view object SLAM without prior object model, and demonstrate that the two aspects can benefit each other. For 3D detection, we generate high quality cuboid proposals from 2D bounding boxes and vanishing points sampling. The proposals are further scored and selected to align with image edges. Experiments on SUN RGBD and KITTI shows the efficiency and accuracy over existing approaches. Then in the second part, multi-view bundle adjustment with novel measurement functions is proposed to jointly optimize camera poses, objects and points, utilizing single view detection results. Objects can provide more geometric constraints and scale consistency compared to points. On the collected and public TUM and KITTI odometry datasets, we achieve better pose estimation accuracy over the state-of-the-art monocular SLAM while also improve the 3D object detection accuracy at the same time.

Includes members:
  Member "stereo vision based semantic 3d object and ego motion tracking for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "robust dense mapping for large scale dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks")
  Member "quadricslam dual quadrics from object detections as landmarks in object oriented slam":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans")
  Member "deep virtual stereo odometry leveraging deep depth prediction for monocular direct sparse odometry":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "bayesian scale estimation for monocular slam based on generic object detection for correcting scale drift":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "direct monocular odometry using points and lines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cubeslam monocular 3 d object slam")
  Member "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "pop up slam semantic monocular plane slam for low texture environments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cubeslam monocular 3 d object slam")

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.RO(Robotics)