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: "a coarse to fine model for 3d pose estimation and sub category recognition"
  Abstract: Despite the fact that object detection, 3D pose estimation, and sub-category recognition are highly correlated tasks, they are usually addressed independently from each other because of the huge space of parameters. To jointly model all of these tasks, we propose a coarse-to-fine hierarchical representation, where each level of the hierarchy represents objects at a different level of granularity. The hierarchical representation prevents performance loss, which is often caused by the increase in the number of parameters (as we consider more tasks to model), and the joint modelling enables resolving ambiguities that exist in independent modelling of these tasks. We augment PASCAL3D+ dataset with annotations for these tasks and show that our hierarchical model is effective in joint modelling of object detection, 3D pose estimation, and sub-category recognition.

Connected through hyperedge HE-1 (induced by source paper "indoor scene understanding in 2 5 3d for autonomous agents a survey"):
  with members:
    - "rgbd salient object detection via deep fusion"
    - "octnetfusion learning depth fusion from data"
    - "3dcontextnet k d tree guided hierarchical learning of point clouds using local and global contextual cues"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning rgb d salient object detection using background enclosure depth contrast and top down features"
    - "matterport3d learning from rgb d data in indoor environments"

Connected through hyperedge HE-2 (induced by source paper "from points to parts 3d object detection from point cloud with part aware and part aggregation network"):
  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"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "associatively segmenting instances and semantics in point clouds"
    - "3d instance segmentation via multi task metric learning"
    - "fishnet a versatile backbone for image region and pixel level prediction"
    - "monocular 3d object detection leveraging accurate proposals and shape reconstruction"

Connected through hyperedge HE-3 (induced by source paper "location field descriptors single image 3d model retrieval in the wild"):
  with members:
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "triplet center loss for multi view 3d object retrieval"
    - "3d pose estimation for fine grained object categories"
    - "learning shape priors for single view 3d completion and reconstruction"
    - "holistic 3d scene parsing and reconstruction from a single rgb image"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-4 (induced by source paper "gp2c geometric projection parameter consensus for joint 3d pose and focal length estimation in the wild"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "3d pose estimation and 3d model retrieval for objects in the wild"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "3d pose estimation for fine grained object categories"
    - "a unified framework for multi view multi class object pose estimation"
    - "ipose instance aware 6d pose estimation of partly occluded objects"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"

Connected through hyperedge HE-5 (induced by source paper "monocular plan view networks for autonomous driving"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "end to end learning of multi sensor 3d tracking by detection"
    - "conditional affordance learning for driving in urban environments"
    - "driving policy transfer via modularity and abstraction"
    - "deep object centric policies for autonomous driving"
    - "on offline evaluation of vision based driving models"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "deep object centric representations for generalizable robot learning"

Connected through hyperedge HE-6 (induced by source paper "mono3d monocular 3d vehicle detection with two scale 3d hypotheses and task priors"):
  with members:
    - "visual inertial object detection and mapping"
    - "frustum pointnets for 3d object detection from rgb d data"
    - "pointfusion deep sensor fusion for 3d bounding box estimation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "accurate single stage detector using recurrent rolling convolution"
    - "stacked hourglass networks for human pose estimation"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-7 (induced by source paper "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "deep ordinal regression network for monocular depth estimation"
    - "vehicle pose and shape estimation through multiple monocular vision"
    - "3d pose estimation for fine grained object categories"
    - "attention is all you need"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"
    - "cascaded pyramid network for multi person pose estimation"

Connected through hyperedge HE-8 (induced by source paper "pointrcnn 3d object proposal generation and detection from point cloud"):
  with members:
    - "pixor real time 3d object detection from point clouds"
    - "gs3d an efficient 3d object detection framework for autonomous driving"
    - "feature intertwiner for object detection"
    - "yolov3 an incremental improvement"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "pointsift a sift like network module for 3d point cloud semantic segmentation"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "voxelnet end to end learning for point cloud based 3d object detection"

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)