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: "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
  Abstract: We propose a stereo vision-based approach for tracking the camera ego-motion and 3D semantic objects in dynamic autonomous driving scenarios. Instead of directly regressing the 3D bounding box using end-to-end approaches, we propose to use the easy-to-labeled 2D detection and discrete viewpoint classification together with a light-weight semantic inference method to obtain rough 3D object measurements. Based on the object-aware-aided camera pose tracking which is robust in dynamic environments, in combination with our novel dynamic object bundle adjustment (BA) approach to fuse temporal sparse feature correspondences and the semantic 3D measurement model, we obtain 3D object pose, velocity and anchored dynamic point cloud estimation with instance accuracy and temporal consistency. The performance of our proposed method is demonstrated in diverse scenarios. Both the ego-motion estimation and object localization are compared with the state-of-of-the-art solutions.

Connected through hyperedge HE-1 (induced by source paper "3d dynamic scene graphs actionable spatial perception with places objects and humans"):
  with members:
    - "teaser fast and certifiable point cloud registration"
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "panoptic segmentation"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "semi supervised semantic mapping through label propagation with semantic texture meshes"
    - "kimera an open source library for real time metric semantic localization and mapping"
    - "3 d scene graph a sparse and semantic representation of physical environments for intelligent agents"
    - "value iteration networks on multiple levels of abstraction"

Connected through hyperedge HE-2 (induced by source paper "zoomnet part aware adaptive zooming neural network for 3d object detection"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "from points to parts 3d object detection from point cloud with part aware and part aggregation network"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "directshape photometric alignment of shape priors for visual vehicle pose and shape estimation"
    - "monocular 3d object detection with pseudo lidar point cloud"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "accurate monocular object detection via color embedded 3d reconstruction for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "dymslam 4d dynamic scene reconstruction based on geometrical motion segmentation"):
  with members:
    - "track to reconstruct and reconstruct to track"
    - "stereo based multi motion visual odometry for mobile robots"
    - "unsupervised monocular depth and ego motion learning with structure and semantics"
    - "learning monocular visual odometry with dense 3d mapping from dense 3d flow"
    - "ds slam a semantic visual slam towards dynamic environments"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-4 (induced by source paper "multi object monocular slam for dynamic environments"):
  with members:
    - "cubeslam monocular 3 d object slam"
    - "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-5 (induced by source paper "refinedmpl refined monocular pseudolidar for 3d object detection in autonomous driving"):
  with members:
    - "from big to small multi scale local planar guidance for monocular depth estimation"
    - "panoptic segmentation"
    - "deep optics for monocular depth estimation and 3d object detection"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "disentangling monocular 3d object detection"
    - "pseudo lidar accurate depth for 3d object detection in autonomous driving"
    - "monocular 3d object detection with pseudo lidar point cloud"

Connected through hyperedge HE-6 (induced by source paper "multi sensor 3d object box refinement for autonomous driving"):
  with members:
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "frustum convnet sliding frustums to aggregate local point wise features for amodal 3d object detection"
    - "triangulation learning network from monocular to stereo 3d object detection"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"
    - "pointpillars fast encoders for object detection from point clouds"
    - "monogrnet a geometric reasoning network for monocular 3d object localization"

Connected through hyperedge HE-7 (induced by source paper "poirot a rotation invariant omni directional pointnet"):
  with members:
    - "multiresolution tree networks for 3d point cloud processing"
    - "a cnn for homogneous riemannian manifolds with applications to neuroimaging"
    - "splatnet sparse lattice networks for point cloud processing"
    - "learning and matching multi view descriptors for registration of point clouds"
    - "fully convolutional point networks for large scale point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "h cnns convolutional neural networks for riemannian homogeneous spaces"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-8 (induced by source paper "visual inertial navigation a concise review"):
  with members:
    - "observability aware self calibration of visual and inertial sensors for ego motion estimation"
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "continuous time visual inertial odometry for event cameras"
    - "visual inertial object detection and mapping"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "closed form solution to cooperative visual inertial structure from motion"
    - "robocentric visual inertial odometry"
    - "relocalization global optimization and map merging for monocular visual inertial slam"

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.CV(Computer Vision and Pattern Recognition)