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: "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.

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)