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: "learning descriptors for object recognition and 3d pose estimation"
  Abstract: Detecting poorly textured objects and estimating their 3D pose reliably is still a very challenging problem. We introduce a simple but powerful approach to computing descriptors for object views that efficiently capture both the object identity and 3D pose. By contrast with previous manifold-based approaches, we can rely on the Euclidean distance to evaluate the similarity between descriptors, and therefore use scalable Nearest Neighbor search methods to efficiently handle a large number of objects under a large range of poses. To achieve this, we train a Convolutional Neural Network to compute these descriptors by enforcing simple similarity and dissimilarity constraints between the descriptors. We show that our constraints nicely untangle the images from different objects and different views into clusters that are not only well-separated but also structured as the corresponding sets of poses: The Euclidean distance between descriptors is large when the descriptors are from different objects, and directly related to the distance between the poses when the descriptors are from the same object. These important properties allow us to outperform state-of-the-art object views representations on challenging RGB and RGB-D data.

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"
    - "normalized object coordinate space for category level 6d object pose and size estimation"

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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "pose augmentation class agnostic object pose transformation for object recognition"):
  with members:
    - "collagan collaborative gan for missing image data imputation"
    - "a style based generator architecture for generative adversarial networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "segmap segment based mapping and localization using data driven descriptors"):
  with members:
    - "segmap 3d segment mapping using data driven descriptors"
    - "learning a local feature descriptor for 3d lidar scans"
    - "seeing the wood for the trees reliable localization in urban and natural environments"
    - "in defense of the triplet loss for person re identification"
    - "semantic visual localization"
    - "3d semantic segmentation with submanifold sparse convolutional networks"
    - "segcloud semantic segmentation of 3d point clouds"
    - "compressed voxel based mapping using unsupervised learning"

Connected through hyperedge HE-5 (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-6 (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"
    - "normalized object coordinate space for category level 6d object pose and size 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"

Connected through hyperedge HE-7 (induced by source paper "dynamic slam the need for speed"):
  with members:
    - "deep robust single image depth estimation neural network using scene understanding"
    - "cubeslam monocular 3 d object slam"
    - "camera based vehicle velocity estimation from monocular video"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "multimotion visual odometry mvo simultaneous estimation of camera and third party motions"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "mask r cnn"
    - "se3 nets learning rigid body motion using deep neural networks"

Connected through hyperedge HE-8 (induced by source paper "segmenting unseen industrial components in a heavy clutter using rgb d fusion and synthetic data"):
  with members:
    - "large scale 6d object pose estimation dataset for industrial bin picking"
    - "deep surface normal estimation with hierarchical rgb d fusion"
    - "the best of both modes separately leveraging rgb and depth for unseen object instance segmentation"
    - "photorealistic image synthesis for object instance detection"
    - "segmenting unknown 3d objects from real depth images using mask r cnn trained on synthetic data"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "mask r cnn"
    - "feature pyramid networks 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)