human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "satellite pose estimation challenge dataset competition design and results"):
  Abstract: Reliable pose estimation of uncooperative satellites is a key technology for enabling future on-orbit servicing and debris removal missions. The Kelvins Satellite Pose Estimation Challenge aims at evaluating and comparing monocular vision-based approaches and pushing the state-of-the-art on this problem. This work is based on the Satellite Pose Estimation Dataset, the first publicly available machine learning set of synthetic and real spacecraft imagery. The choice of dataset reflects one of the unique challenges associated with spaceborne computer vision tasks, namely the lack of spaceborne images to train and validate the developed algorithms. This work briefly reviews the basic properties and the collection process of the dataset which was made publicly available. The competition design, including the definition of performance metrics and the adopted testbed, is also discussed. Furthermore, the submissions of the 48 participants are analyzed to compare the performance of their approaches and uncover what factors make the satellite pose estimation problem especially challenging.

Includes members:
  Member "pose estimation for non cooperative rendezvous using neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "assistive relative pose estimation for on orbit assembly using convolutional neural networks")
  Member "towards robust learning based pose estimation of noncooperative spacecraft":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "assistive relative pose estimation for on orbit assembly using convolutional neural networks")
  Member "implicit 3d orientation learning for 6d object detection from rgb images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "pvnet pixel wise voting network for 6dof pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "estimating 6d pose from localizing designated surface keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a markerless deep learning based 6 degrees of freedom poseestimation for with mobile robots using rgb data")
  Member "3d pose regression using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "6d object pose regression via supervised learning on point clouds")
  Member "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "real time seamless single shot 6d object pose prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")

Question: Please tell me which class the source paper 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)