human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "integrating objects into monocular slam line based category specific models"):
  with members:
    - "constructing category specific models for monocular object slam"
    - "edge slam edge points based monocular visual slam"
    - "slam with objects using a nonparametric pose graph"
    - "object centric photometric bundle adjustment with deep shape prior"
    - "stacked hourglass networks for human pose estimation"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-2 (induced by source paper "probabilistic data association via mixture models for robust semantic slam"):
  with members:
    - "long duration fully autonomous operation of rotorcraft unmanned aerial systems for remote sensing data acquisition"
    - "convex relaxations for pose graph optimization with outliers"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "slam with objects using a nonparametric pose graph"
    - "lift learned invariant feature transform"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-3 (induced by source paper "monocular object and plane slam in structured environments"):
  with members:
    - "cubeslam monocular 3 d object slam"
    - "fusion volumetric object level slam"
    - "structure aware slam using quadrics and planes"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "cooperative holistic scene understanding unifying 3d object layout and camera pose estimation"
    - "joint layout estimation and global multi view registration for indoor reconstruction"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "roomnet end to end room layout estimation"

Question: Please tell me which class the center node 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)