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: "dpod 6d pose object detector and refiner"
  Abstract: In this paper we present a novel deep learning method for 3D object detection and 6D pose estimation from RGB images. Our method, named DPOD (Dense Pose Object Detector), estimates dense multi-class 2D-3D correspondence maps between an input image and available 3D models. Given the correspondences, a 6DoF pose is computed via PnP and RANSAC. An additional RGB pose refinement of the initial pose estimates is performed using a custom deep learning-based refinement scheme. Our results and comparison to a vast number of related works demonstrate that a large number of correspondences is beneficial for obtaining high-quality 6D poses both before and after refinement. Unlike other methods that mainly use real data for training and do not train on synthetic renderings, we perform evaluation on both synthetic and real training data demonstrating superior results before and after refinement when compared to all recent detectors. While being precise, the presented approach is still real-time capable.

Connected through hyperedge HE-1 (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"
    - "metagrasp data efficient grasping by affordance interpreter network"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "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-2 (induced by source paper "robust 6d object pose estimation by learning rgb d features"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "explaining the ambiguity of object detection and 6d pose from visual data"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "pvnet pixel wise voting network for 6dof pose estimation"

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"
    - "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"
    - "objects as points"

Connected through hyperedge HE-4 (induced by source paper "deepurl deep pose estimation framework for underwater relative localization"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "beyond sharing weights for deep domain adaptation"
    - "cullnet calibrated and pose aware confidence scores for object pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "yolov3 an incremental improvement"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "segmentation driven 6d object pose estimation"
    - "making deep heatmaps robust to partial occlusions for 3d object pose estimation"

Connected through hyperedge HE-5 (induced by source paper "deceptionnet network driven domain randomization"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep model based 6d pose refinement in rgb"
    - "learning to adapt structured output space for semantic segmentation"
    - "learning dexterous in hand manipulation"
    - "adversarial patch"
    - "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"
    - "learning semantic segmentation from synthetic data a geometrically guided input output adaptation approach"

Connected through hyperedge HE-6 (induced by source paper "single stage 6d object pose estimation"):
  with members:
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "neural guided ransac learning where to sample model hypotheses"
    - "backpropagation friendly eigendecomposition"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "lf net learning local features from images"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "yolov3 an incremental improvement"
    - "segmentation driven 6d object pose estimation"

Connected through hyperedge HE-7 (induced by source paper "camera to robot pose estimation from a single image"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "real time 3d traffic cone detection for autonomous driving"
    - "deep high resolution representation learning for human pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "real time highly accurate robotic grasp detection using fully convolutional neural networks with high resolution images"
    - "simple baselines for human pose estimation and tracking"
    - "synthetically trained neural networks for learning human readable plans from real world demonstrations"

Connected through hyperedge HE-8 (induced by source paper "verefine integrating object pose verification with iterative physics guided refinement"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "graspa 1 0 graspa is a robot arm grasping performance benchmark"
    - "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household 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)