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: "estimating 6d pose from localizing designated surface keypoints"
  Abstract: In this paper, we present an accurate yet effective solution for 6D pose estimation from an RGB image. The core of our approach is that we first designate a set of surface points on target object model as keypoints and then train a keypoint detector (KPD) to localize them. Finally a PnP algorithm can recover the 6D pose according to the 2D-3D relationship of keypoints. Different from recent state-of-the-art CNN-based approaches that rely on a time-consuming post-processing procedure, our method can achieve competitive accuracy without any refinement after pose prediction. Meanwhile, we obtain a 30% relative improvement in terms of ADD accuracy among methods without using refinement. Moreover, we succeed in handling heavy occlusion by selecting the most confident keypoints to recover the 6D pose. For the sake of reproducibility, we will make our code and models publicly available soon.

Connected through hyperedge HE-1 (induced by source paper "a markerless deep learning based 6 degrees of freedom poseestimation for with mobile robots using rgb data"):
  with members:
    - "a 3d deep learning based augmented reality calibration method for robotic environments using depth sensor data"
    - "augmented reality based visualization of navigation data of mobile robots on the microsoft hololens possibilities and limitations"
    - "deep hough voting for 3d object detection in point clouds"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "deep 6 dof tracking"
    - "real time seamless single shot 6d object pose prediction"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "towards robust learning based pose estimation of noncooperative spacecraft"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "pose estimation for non cooperative rendezvous using neural networks"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "yolov3 an incremental improvement"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "pose estimation for non cooperative spacecraft rendezvous using convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "graspnet a large scale clustered and densely annotated datase for object grasping"):
  with members:
    - "self supervised 6d object pose estimation for robot manipulation"
    - "s4g amodal single view single shot se 3 grasp detection in cluttered scenes"
    - "roi based robotic grasp detection for object overlapping scenes"
    - "6 dof graspnet variational grasp generation for object manipulation"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "closing the loop for robotic grasping a real time generative grasp synthesis approach"
    - "pointnetgpd detecting grasp configurations from point sets"
    - "roi based robotic grasp detection in object overlapping scenes using convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "satellite pose estimation challenge dataset competition design and results"):
  with members:
    - "pose estimation for non cooperative rendezvous using neural networks"
    - "towards robust learning based pose estimation of noncooperative spacecraft"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "pvnet pixel wise voting network for 6dof pose estimation"
    - "3d pose regression using convolutional neural networks"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "real time seamless single shot 6d object pose prediction"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

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)