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: "gogma globally optimal gaussian mixture alignment"
  Abstract: Gaussian mixture alignment is a family of approaches that are frequently used for robustly solving the point-set registration problem. However, since they use local optimisation, they are susceptible to local minima and can only guarantee local optimality. Consequently, their accuracy is strongly dependent on the quality of the initialisation. This paper presents the first globally-optimal solution to the 3D rigid Gaussian mixture alignment problem under the L2 distance between mixtures. The algorithm, named GOGMA, employs a branch-and-bound approach to search the space of 3D rigid motions SE(3), guaranteeing global optimality regardless of the initialisation. The geometry of SE(3) was used to find novel upper and lower bounds for the objective function and local optimisation was integrated into the scheme to accelerate convergence without voiding the optimality guarantee. The evaluation empirically supported the optimality proof and showed that the method performed much more robustly on two challenging datasets than an existing globally-optimal registration solution.

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 "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"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "segicp integrated deep semantic segmentation and pose estimation"
    - "rfm slam exploiting relative feature measurements to separate orientation and position estimation in slam"
    - "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-3 (induced by source paper "can generalised relative pose estimation solve sparse 3d registration"):
  with members:
    - "deepmapping unsupervised map estimation from multiple point clouds"
    - "imls slam scan to model matching based on 3d data"
    - "floornet a unified framework for floorplan reconstruction from 3d scans"
    - "learning compact geometric features"
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "multi image matching via fast alternating minimization"

Connected through hyperedge HE-4 (induced by source paper "the alignment of the spheres globally optimal spherical mixture alignment for camera pose estimation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "tangent convolutions for dense prediction in 3d"
    - "recurrent slice networks for 3d segmentation on point clouds"
    - "geometric loss functions for camera pose regression with deep learning"
    - "large scale point cloud semantic segmentation with superpoint graphs"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"

Connected through hyperedge HE-5 (induced by source paper "iterative transformer network for 3d point cloud"):
  with members:
    - "dynamic graph cnn for learning on point clouds"
    - "pcn point completion network"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "orientation boosted voxel nets for 3d object recognition"
    - "inverse compositional spatial transformer networks"
    - "tensorflow a system for large scale machine learning"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-6 (induced by source paper "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"):
  with members:
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "an adaptive data representation for robust point set registration and merging"
    - "point set registration coherent point drift"

Connected through hyperedge HE-7 (induced by source paper "density adaptive point set registration"):
  with members:
    - "an adaptive data representation for robust point set registration and merging"
    - "point set registration coherent point drift"

Connected through hyperedge HE-8 (induced by source paper "3d registration of curves and surfaces using local differential information"):
  with members:
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"
    - "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)