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 "bop benchmark for 6d object pose estimation"):
  Abstract: We propose a benchmark for 6D pose estimation of a rigid object from a single RGB-D input image. The training data consists of a texture-mapped 3D object model or images of the object in known 6D poses. The benchmark comprises of: i) eight datasets in a unified format that cover different practical scenarios, including two new datasets focusing on varying lighting conditions, ii) an evaluation methodology with a pose-error function that deals with pose ambiguities, iii) a comprehensive evaluation of 15 diverse recent methods that captures the status quo of the field, and iv) an online evaluation system that is open for continuous submission of new results. The evaluation shows that methods based on point-pair features currently perform best, outperforming template matching methods, learning-based methods and methods based on 3D local features. The project website is available at this http URL.

Includes members:
  Member "6d pose estimation using an improved method based on point pair features":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "vision based robotic grasping from object localization pose estimation grasp detection to motion planning a review")
  Member "rotational subgroup voting and pose clustering for robust 3d object recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust occlusion aware pose estimation for objects grasped by adaptive hands")
  Member "t less an rgb d dataset for 6d pose estimation of texture less objects":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  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-4 (induced by source paper "neural mesh refiner for 6 dof pose estimation")
  Member "ssd 6d making rgb based 3d detection and 6d pose estimation great again":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "deep learning of local rgb d patches for 3d object detection and 6d pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "global hypothesis generation for 6d object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "going further with point pair features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deepim deep iterative matching for 6d pose estimation")

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)