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 3 d object slam"
  Abstract: In this paper, we present a method for single image three-dimensional (3-D) cuboid object detection and multiview object simultaneous localization and mapping in both static and dynamic environments, and demonstrate that the two parts can improve each other. First, for single image object detection, we generate high-quality cuboid proposals from two-dimensional (2-D) bounding boxes and vanishing points sampling. The proposals are further scored and selected based on the alignment with image edges. Second, multiview bundle adjustment with new object measurements is proposed to jointly optimize poses of cameras, objects, and points. Objects can provide long-range geometric and scale constraints to improve camera pose estimation and reduce monocular drift. Instead of treating dynamic regions as outliers, we utilize object representation and motion model constraints to improve the camera pose estimation. The 3-D detection experiments on SUN RGBD and KITTI show better accuracy and robustness over existing approaches. On the public TUM, KITTI odometry and our own collected datasets, our SLAM method achieves the state-of-the-art monocular camera pose estimation and at the same time, improves the 3-D object detection accuracy.

Connected through hyperedge HE-1 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "foveabox beyond anchor based object detector"
    - "pixor real time 3d object detection from point clouds"
    - "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"
    - "shift r cnn deep monocular 3d object detection with closed form geometric constraints"
    - "deep fitting degree scoring network for monocular 3d object detection"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "fcos fully convolutional one stage object detection"

Connected through hyperedge HE-2 (induced by source paper "multi object monocular slam for dynamic environments"):
  with members:
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "the earth ain t flat monocular reconstruction of vehicles on steep and graded roads from a moving camera"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"
    - "yolo9000 better faster stronger"
    - "reconstructing vechicles from a single image shape priors for road scene understanding"

Connected through hyperedge HE-3 (induced by source paper "dynamic slam the need for speed"):
  with members:
    - "deep robust single image depth estimation neural network using scene understanding"
    - "camera based vehicle velocity estimation from monocular video"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "mask r cnn"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "mot16 a benchmark for multi object tracking"

Connected through hyperedge HE-4 (induced by source paper "gradslam dense slam meets automatic differentiation"):
  with members:
    - "end to end lane detection through differentiable least squares fitting"
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "generalized inner loop meta learning"
    - "deeptam deep tracking and mapping"
    - "regnet learning the optimization of direct image to image pose registration"
    - "constructing category specific models for monocular object slam"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-5 (induced by source paper "robust data association for object level semantic slam"):
  with members:
    - "monocular object and plane slam in structured environments"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "you only look once unified real time object detection"
    - "efficient pattern matching on binary strings"

Connected through hyperedge HE-6 (induced by source paper "textslam visual slam with planar text features"):
  with members:
    - "fusion volumetric object level slam"
    - "monocular object and plane slam in structured environments"
    - "quadricslam dual quadrics from object detections as landmarks in object oriented slam"
    - "east an efficient and accurate scene text detector"
    - "direct sparse odometry"
    - "coco text dataset and benchmark for text detection and recognition in natural images"
    - "orb slam a versatile and accurate monocular slam system"
    - "real time monocular object slam"

Connected through hyperedge HE-7 (induced by source paper "end to end motion planning of quadrotors using deep reinforcement learning"):
  with members:
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "accurate tracking of aggressive quadrotor trajectories using incremental nonlinear dynamic inversion and differential flatness"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "robust stereo visual inertial odometry for fast autonomous flight"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "monocular object and plane slam in structured environments"):
  with members:
    - "fusion volumetric object level slam"
    - "structure aware slam using quadrics and planes"
    - "cubeslam monocular 3d object detection and slam without prior models"
    - "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)