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: "normalized object coordinate space for category level 6d object pose and size estimation"
  Abstract: The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to "instance-level" 6D pose estimation tasks, our problem assumes that no exact object CAD models are available during either training or testing time. To handle different and unseen object instances in a given category, we introduce a Normalized Object Coordinate Space (NOCS)---a shared canonical representation for all possible object instances within a category. Our region-based neural network is then trained to directly infer the correspondence from observed pixels to this shared object representation (NOCS) along with other object information such as class label and instance mask. These predictions can be combined with the depth map to jointly estimate the metric 6D pose and dimensions of multiple objects in a cluttered scene. To train our network, we present a new context-aware technique to generate large amounts of fully annotated mixed reality data. To further improve our model and evaluate its performance on real data, we also provide a fully annotated real-world dataset with large environment and instance variation. Extensive experiments demonstrate that the proposed method is able to robustly estimate the pose and size of unseen object instances in real environments while also achieving state-of-the-art performance on standard 6D pose estimation benchmarks.

Connected through hyperedge HE-1 (induced by source paper "cps class level 6d pose and shape estimation from monocular images"):
  with members:
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "stereo r cnn based 3d object detection for autonomous driving"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "disentangling monocular 3d object detection"
    - "multi path learning for object pose estimation across domains"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "mesh r cnn"
    - "accurate monocular object detection via color embedded 3d reconstruction for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "dgcm net dense geometrical correspondence matching network for incremental experience based robotic grasping"):
  with members:
    - "directional semantic grasping of real world objects from simulation to reality"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "metagrasp data efficient grasping by affordance interpreter network"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"

Connected through hyperedge HE-3 (induced by source paper "mobilepose real time pose estimation for unseen objects with weak shape supervision"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "hybridpose 6d object pose estimation under hybrid representations"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "object as distribution"
    - "mediapipe a framework for building perception pipelines"
    - "centernet keypoint triplets for object detection"
    - "implicit 3d orientation learning for 6d object detection from rgb images"

Connected through hyperedge HE-4 (induced by source paper "learning canonical shape space for category level 6d object pose and size estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "6 pack category level 6d pose tracker with anchor based keypoints"
    - "real time 6d object pose estimation on cpu"
    - "instance and category level 6d object pose estimation"
    - "category level 6d object pose recovery in depth images"
    - "group normalization"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "matching rgb images to cad models for object pose estimation"

Connected through hyperedge HE-5 (induced by source paper "active 6d multi object pose estimation in cluttered scenarios with deep reinforcement learning"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "instance and category level 6d object pose estimation"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "bop benchmark for 6d object pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "multi task deep networks for depth based 6d object pose and joint registration in crowd scenarios"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "deep object centric policies for autonomous driving"

Connected through hyperedge HE-6 (induced by source paper "nasa neural articulated shape approximation"):
  with members:
    - "implicit functions in feature space for 3d shape reconstruction and completion"
    - "amass archive of motion capture as surface shapes"
    - "deferred neural rendering image synthesis using neural textures"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "cvxnets learnable convex decomposition"
    - "unsupervised part based disentangling of object shape and appearance"
    - "what do single view 3d reconstruction networks learn"
    - "cerberus a multi headed derenderer"

Connected through hyperedge HE-7 (induced by source paper "multiview aggregation for learning category specific shape reconstruction"):
  with members:
    - "equivariant multi view networks"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning single image 3d reconstruction by generative modelling of shape pose and shading"
    - "learning category specific mesh reconstruction from image collections"
    - "deepmvs learning multi view stereopsis"
    - "learning 3d shapes as multi layered height maps using 2d convolutional networks"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "deep sets"

Connected through hyperedge HE-8 (induced by source paper "location field descriptors single image 3d model retrieval in the wild"):
  with members:
    - "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"
    - "ipose instance aware 6d pose estimation of partly occluded objects"

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)