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: "in search of inliers 3d correspondence by local and global voting"
  Abstract: We present a method for finding correspondence between 3D models. From an initial set of feature correspondences, our method uses a fast voting scheme to separate the inliers from the outliers. The novelty of our method lies in the use of a combination of local and global constraints to determine if a vote should be cast. On a local scale, we use simple, low-level geometric invariants. On a global scale, we apply covariant constraints for finding compatible correspondences. We guide the sampling for collecting voters by downward dependencies on previous voting stages. All of this together results in an accurate matching procedure. We evaluate our algorithm by controlled and comparative testing on different datasets, giving superior performance compared to state of the art methods. In a final experiment, we apply our method for 3D object detection, showing potential use of our method within higher-level vision.

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"
    - "performance evaluation of 3d correspondence grouping algorithms"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a performance evaluation of correspondence grouping methods for 3d rigid data matching"):
  with members:
    - "performance evaluation of 3d correspondence grouping algorithms"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "matchbench an evaluation of feature matchers"
    - "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:
    - "performance evaluation of 3d correspondence grouping algorithms"
    - "rotational subgroup voting and pose clustering for robust 3d object recognition"

Connected through hyperedge HE-4 (induced by source paper "performance evaluation of 3d correspondence grouping algorithms"):
  with members:
    - "rotational projection statistics for 3d local surface description and object recognition"
    - "shrec 2011 robust feature detection and description benchmark"

Connected through hyperedge HE-5 (induced by source paper "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"):
  with members:
    - "hashmod a hashing method for scalable 3d object detection"
    - "learning descriptors for object recognition and 3d pose estimation"
    - "learning rich features from rgb d images for object detection and segmentation"
    - "caffe convolutional architecture for fast feature embedding"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "hashmod a hashing method for scalable 3d object detection"):
  with members:
    - "fast supervised hashing with decision trees for high dimensional data"
    - "fast exact search in hamming space with multi index hashing"

Connected through hyperedge HE-7 (induced by source paper "6d object detection and next best view prediction in the crowd"):
  with members:
    - "learning descriptors for object recognition and 3d pose estimation"
    - "learning to compare image patches via convolutional neural networks"
    - "deepid net deformable deep convolutional neural networks for object detection"
    - "3d shapenets a deep representation for volumetric shapes"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-8 (induced by source paper "recovering 6d object pose and predicting next best view in the crowd"):
  with members:
    - "learning descriptors for object recognition and 3d pose estimation"
    - "learning to compare image patches via convolutional neural networks"
    - "deepid net deformable deep convolutional neural networks for object detection"
    - "3d shapenets a deep representation for volumetric shapes"

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)