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: "6 dof object pose from semantic keypoints"
  Abstract: This paper presents a novel approach to estimating the continuous six degree of freedom (6-DoF) pose (3D translation and rotation) of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. Unlike prior work, we are agnostic to whether the object is textured or textureless, as the convnet learns the optimal representation from the available training image data. Furthermore, the approach can be applied to instance- and class-based pose recovery. Empirically, we show that the proposed approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios with a cluttered background. For class-based object pose estimation, state-of-the-art accuracy is shown on the large-scale PASCAL3D+ dataset.

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of category specific symmetric 3d keypoints from point sets"):
  with members:
    - "usip unsupervised stable interest point detection from 3d point clouds"
    - "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"
    - "c3dpo canonical 3d pose networks for non rigid structure from motion"
    - "cycle in cycle generative adversarial networks for keypoint guided image generation"
    - "deep non rigid structure from motion"
    - "pytorch an imperative style high performance deep learning library"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"

Connected through hyperedge HE-2 (induced by source paper "deepurl deep pose estimation framework for underwater relative localization"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "keypointnet a large scale 3d keypoint dataset aggregated from numerous human annotations"):
  with members:
    - "spair 71k a large scale benchmark for semantic correspondence"
    - "relation shape convolutional neural network for point cloud analysis"
    - "spidercnn deep learning on point sets with parameterized convolutional filters"
    - "pointwise rotation invariant network with adaptive sampling and 3d spherical voxel convolution"
    - "deep functional dictionaries learning consistent semantic structures on 3d models from functions"
    - "pointconv deep convolutional networks on 3d point clouds"
    - "dynamic graph cnn for learning on point clouds"
    - "recurrent slice networks for 3d segmentation on point clouds"

Connected through hyperedge HE-4 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "conceptualization of object compositions using persistent homology"
    - "admissible abstractions for near optimal task and motion planning"
    - "visual memory for robust path following"
    - "yolov3 an incremental improvement"
    - "sampling based methods for factored task and motion planning"
    - "fast autonomous flight in gps denied and cluttered environments"
    - "cognitive mapping and planning for visual navigation"

Connected through hyperedge HE-5 (induced by source paper "yoloff you only learn offsets for robust 6dof object pose estimation"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pix2pose pixel wise coordinate regression of objects for 6d pose estimation"
    - "h o unified egocentric recognition of 3d hand object poses and interactions"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "open3d a modern library for 3d data processing"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "deep 6dpose recovering 6d object pose from a single rgb image"

Connected through hyperedge HE-6 (induced by source paper "technical report reactive semantic planning in unexplored semantic environments using deep perceptual feedback"):
  with members:
    - "reactive navigation in partially familiar planar environments using semantic perceptual feedback"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "neural autonomous navigation with riemannian motion policy"
    - "yolov3 an incremental improvement"
    - "semi parametric topological memory for navigation"
    - "high speed flight in an ergodic forest"

Connected through hyperedge HE-7 (induced by source paper "mesh r cnn"):
  with members:
    - "learning to infer and execute 3d shape programs"
    - "learning category specific mesh reconstruction from image collections"
    - "splatnet sparse lattice networks for point cloud processing"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "pix3d dataset and methods for single image 3d shape modeling"
    - "tangent convolutions for dense prediction in 3d"
    - "occupancy networks learning 3d reconstruction in function space"

Connected through hyperedge HE-8 (induced by source paper "bottom up object detection by grouping extreme and center points"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "personlab person pose estimation and instance segmentation with a bottom up part based geometric embedding model"
    - "yolov3 an incremental improvement"
    - "simple baselines for human pose estimation and tracking"
    - "acquisition of localization confidence for accurate object detection"
    - "starmap for category agnostic keypoint and viewpoint estimation"
    - "path aggregation network for instance segmentation"
    - "couplenet coupling global structure with local parts for 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)