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: "guaranteed outlier removal for point cloud registration with correspondences"
  Abstract: An established approach for 3D point cloud registration is to estimate the registration function from 3D keypoint correspondences. Typically, a robust technique is required to conduct the estimation, since there are false correspondences or outliers. Current 3D keypoint techniques are much less accurate than their 2D counterparts, thus they tend to produce extremely high outlier rates. A large number of putative correspondences must thus be extracted to ensure that sufficient good correspondences are available. Both factors (high outlier rates, large data sizes) however cause existing robust techniques to require very high computational cost. In this paper, we present a novel preprocessing method called  guaranteed outlier removal  for point cloud registration. Our method reduces the input to a smaller set, in a way that any rejected correspondence is guaranteed to not exist in the globally optimal solution. The reduction is performed using purely geometric operations which are deterministic and fast. Our method significantly reduces the population of outliers, such that further optimization can be performed quickly. Further, since only true outliers are removed, the globally optimal solution is preserved. On various synthetic and real data experiments, we demonstrate the effectiveness of our preprocessing method.  Demo code is available as supplementary material , which can be found on the Computer Society Digital Library at   http://doi.ieeecomputersociety.org/10.1109/TPAMI.2017.2773482 .

Connected through hyperedge HE-1 (induced by source paper "teaser fast and certifiable point cloud registration"):
  with members:
    - "did you miss the sign a false negative alarm system for traffic sign detectors"
    - "consensus maximization tree search revisited"
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "a practical maximum clique algorithm for matching with pairwise constraints"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "prnet self supervised learning for partial to partial registration"
    - "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"

Connected through hyperedge HE-2 (induced by source paper "3d local features for direct pairwise registration"):
  with members:
    - "ppfnet global context aware local features for robust 3d point matching"
    - "latent ransac"
    - "bop benchmark for 6d object pose estimation"
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "3dfeat net weakly supervised local 3d features for point cloud registration"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "ppf foldnet unsupervised learning of rotation invariant 3d local descriptors"
    - "cubenet equivariance to 3d rotation and translation"

Connected through hyperedge HE-3 (induced by source paper "a polynomial time solution for robust registration with extreme outlier rates"):
  with members:
    - "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "rfm slam exploiting relative feature measurements to separate orientation and position estimation in slam"
    - "gogma globally optimal gaussian mixture alignment"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "fast algorithms for the maximum clique problem on massive graphs with applications to overlapping community detection"

Connected through hyperedge HE-4 (induced by source paper "graduated non convexity for robust spatial perception from non minimal solvers to global outlier rejection"):
  with members:
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "cvxpnpl a unified convex solution to the absolute pose estimation problem from point and line correspondences"
    - "convex relaxations for pose graph optimization with outliers"
    - "accelerated inference in markov random fields via smooth riemannian optimization"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "robust fitting in computer vision easy or hard"
    - "rotation averaging and strong duality"
    - "sparse representation for 3d shape estimation a convex relaxation approach"

Connected through hyperedge HE-5 (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"
    - "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-6 (induced by source paper "target less registration of point clouds a review"):
  with members:
    - "the perfect match 3d point cloud matching with smoothed densities"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "rotational projection statistics for 3d local surface description and object recognition"
    - "point set registration coherent point drift"

Connected through hyperedge HE-7 (induced by source paper "practical optimal registration of terrestrial lidar scan pairs"):
  with members:
    - "robust fitting in computer vision easy or hard"
    - "go icp a globally optimal solution to 3d icp point set registration"

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)