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: "an adaptive data representation for robust point set registration and merging"
  Abstract: This paper presents a framework for rigid point-set registration and merging using a robust continuous data representation. Our point-set representation is constructed by training a one-class support vector machine with a Gaussian radial basis function kernel and subsequently approximating the output function with a Gaussian mixture model. We leverage the representation's sparse parametrisation and robustness to noise, outliers and occlusions in an efficient registration algorithm that minimises the L2 distance between our support vector -- parametrised Gaussian mixtures. In contrast, existing techniques, such as Iterative Closest Point and Gaussian mixture approaches, manifest a narrower region of convergence and are less robust to occlusions and missing data, as demonstrated in the evaluation on a range of 2D and 3D datasets. Finally, we present a novel algorithm, GMMerge, that parsimoniously and equitably merges aligned mixture models, allowing the framework to be used for reconstruction and mapping.

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"
    - "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"

Connected through hyperedge HE-3 (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-4 (induced by source paper "filterreg robust and efficient probabilistic point set registration using gaussian filter and twist parameterization"):
  with members:
    - "surfelwarp efficient non volumetric single view dynamic reconstruction"
    - "fast and accurate point cloud registration using trees of gaussian mixtures"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "point set registration coherent point drift"

Connected through hyperedge HE-5 (induced by source paper "fast and globally optimal rigid registration of 3d point sets by transformation decomposition"):
  with members:
    - "gogma globally optimal gaussian mixture alignment"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "point set registration coherent point drift"

Connected through hyperedge HE-6 (induced by source paper "fast and accurate point cloud registration using trees of gaussian mixtures"):
  with members:
    - "joint alignment of multiple point sets with batch and incremental expectation maximization"
    - "point set registration coherent point drift"
    - "outlier robust icp for minimizing fractional rmsd"

Connected through hyperedge HE-7 (induced by source paper "density adaptive point set registration"):
  with members:
    - "gogma globally optimal gaussian mixture alignment"
    - "point set registration coherent point drift"

Connected through hyperedge HE-8 (induced by source paper "dugma dynamic uncertainty based gaussian mixture alignment"):
  with members:
    - "gogma globally optimal gaussian mixture alignment"

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)