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: "performance evaluation of 3d correspondence grouping algorithms"
  Abstract: This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences. A good correspondence grouping algorithm is desired to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall. Towards this rule, we deploy the experiments on three benchmarks respectively addressing shape retrieval, 3D object recognition and point cloud registration scenarios. The variety in application context brings a rich category of nuisances including noise, varying point densities, clutter, occlusion and partial overlaps. It also results to different ratios of inliers and correspondence distributions for comprehensive evaluation. Based on the quantitative outcomes, we give a summarization of the merits/demerits of the evaluated algorithms from both performance and efficiency perspectives.

Connected through hyperedge HE-1 (induced by source paper "object pose estimation in robotics revisited"):
  with members:
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"
    - "ssd 6d making rgb based 3d detection and 6d pose estimation great again"
    - "high precision grasp pose detection in dense clutter"
    - "recovering 6d object pose and predicting next best view in the crowd"

Connected through hyperedge HE-2 (induced by source paper "a performance evaluation of correspondence grouping methods for 3d rigid data matching"):
  with members:
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "matchbench an evaluation of feature matchers"
    - "in search of inliers 3d correspondence by local and global voting"
    - "rotational projection statistics for 3d local surface description and object recognition"
    - "shrec 2011 robust feature detection and description benchmark"

Connected through hyperedge HE-3 (induced by source paper "performance comparison of 3d correspondence grouping algorithm for 3d plant point clouds"):
  with members:
    - "rotational subgroup voting and pose clustering for robust 3d object recognition"
    - "in search of inliers 3d correspondence by local and global voting"

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)