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: "a polynomial time solution for robust registration with extreme outlier rates"
  Abstract: We propose a robust approach for the registration of two sets of 3D points in the presence of a large amount of outliers. Our first contribution is to reformulate the registration problem using a Truncated Least Squares (TLS) cost that makes the estimation insensitive to a large fraction of spurious point-to-point correspondences. The second contribution is a general framework to decouple rotation, translation, and scale estimation, which allows solving in cascade for the three transformations. Since each subproblem (scale, rotation, and translation estimation) is still non-convex and combinatorial in nature, out third contribution is to show that (i) TLS scale and (component-wise) translation estimation can be solved exactly and in polynomial time via an adaptive voting scheme, (ii) TLS rotation estimation can be relaxed to a semidefinite program and the relaxation is tight in practice, even in presence of an extreme amount of outliers. We validate the proposed algorithm, named TEASER (Truncated least squares Estimation And SEmidefinite Relaxation), in standard registration benchmarks showing that the algorithm outperforms RANSAC and robust local optimization techniques, and favorably compares with Branch-and-Bound methods, while being a polynomial-time algorithm. TEASER can tolerate up to 99% outliers and returns highly-accurate solutions.

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 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"
    - "open3d a modern library for 3d data processing"

Connected through hyperedge HE-2 (induced by source paper "globally optimal consensus maximization for robust visual inertial localization in point and line map"):
  with members:
    - "2 entity ransac for robust visual localization in changing environment"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "laser map aided visual inertial localization in changing environment"
    - "a minimal closed form solution for multi perspective pose estimation using points and lines"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"
    - "globally optimal inlier set maximisation for simultaneous camera pose and feature correspondence"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-3 (induced by source paper "prnet self supervised learning for partial to partial registration"):
  with members:
    - "kpconv flexible and deformable convolution for point clouds"
    - "deep part induction from articulated object pairs"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "deep closest point learning representations for point cloud registration"
    - "rethinking imagenet pre training"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "point convolutional neural networks by extension operators"

Connected through hyperedge HE-4 (induced by source paper "in perfect shape certifiably optimal 3d shape reconstruction from 2d landmarks"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "learning to reconstruct 3d human pose and shape via model fitting in the loop"
    - "convolutional mesh regression for single image human shape reconstruction"
    - "guaranteed globally optimal planar pose graph and landmark slam via sparse bounded sums of squares programming"
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "sparse representation for 3d shape estimation a convex relaxation approach"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"

Connected through hyperedge HE-5 (induced by source paper "graduated non convexity for robust spatial perception from non minimal solvers to global outlier rejection"):
  with members:
    - "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"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "robust fitting in computer vision easy or hard"
    - "rotation averaging and strong duality"
    - "sparse representation for 3d shape estimation a convex relaxation approach"

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.RO(Robotics)