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 "object pose estimation in robotics revisited"):
  Abstract: Vision-based object grasping and manipulation in robotics require accurate estimation of the object 6D pose. Therefore pose estimation has received significant attention and multiple datasets and evaluation metrics have been proposed. Most of the existing evaluation metrics rank the estimated poses solely based on the visual perspective i.e. how well two geometrical surfaces are aligned, which does not directly indicate the goodness of the pose for a robot manipulation. In robotic manipulation the optimal grasp pose depends on many factors such as target object weight and material, robot, gripper, and the task itself. In this work we address these factors by proposing a probabilistic evaluation metric that ranks an estimated object pose based on the conditional probability of completing a task given this estimated pose. The evaluation metric is validated in controlled experiments and a number of baseline and recent pose estimation methods are compared on a dataset of industrial parts for assembly tasks. The experimental results confirm that the proposed evaluation metric measures the fitness of an estimated pose more accurately for a robotic task compared to prior metrics.

Includes members:
  Member "deep model based 6d pose refinement in rgb":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cps class level 6d pose and shape estimation from monocular images")
  Member "bop benchmark for 6d object pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robust 6d object pose estimation by learning rgb d features")
  Member "performance evaluation of 3d correspondence grouping algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a performance evaluation of correspondence grouping methods for 3d rigid data matching")
  Member "using simulation and domain adaptation to improve efficiency of deep robotic grasping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep reinforcement learning for autonomous driving a survey")
  Member "t less an rgb d dataset for 6d pose estimation of texture less objects":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "learning a visuomotor controller for real world robotic grasping using easily simulated depth images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain independent unsupervised learning to grasp the novel objects")
  Member "ssd 6d making rgb based 3d detection and 6d pose estimation great again":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepim deep iterative matching for 6d pose estimation")
  Member "high precision grasp pose detection in dense clutter":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regnet region based grasp network for single shot grasp detection in point clouds")

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)