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: "globally optimal inlier set maximisation for simultaneous camera pose and feature correspondence"
  Abstract: Estimating the 6-DoF pose of a camera from a single image relative to a pre-computed 3D point-set is an important task for many computer vision applications. Perspective-n-Point (PnP) solvers are routinely used for camera pose estimation, provided that a good quality set of 2D-3D feature correspondences are known beforehand. However, finding optimal correspondences between 2D key-points and a 3D point-set is non-trivial, especially when only geometric (position) information is known. Existing approaches to the simultaneous pose and correspondence problem use local optimisation, and are therefore unlikely to find the optimal solution without a good pose initialisation, or introduce restrictive assumptions. Since a large proportion of outliers are common for this problem, we instead propose a globally-optimal inlier set cardinality maximisation approach which jointly estimates optimal camera pose and optimal correspondences. Our approach employs branch-and-bound to search the 6D space of camera poses, guaranteeing global optimality without requiring a pose prior. The geometry of SE(3) is used to find novel upper and lower bounds for the number of inliers and local optimisation is integrated to accelerate convergence. The evaluation empirically supports the optimality proof and shows that the method performs much more robustly than existing approaches, including on a large-scale outdoor data-set.

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 "learning 2d 3d correspondences to solve the blind perspective n point problem"):
  with members:
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "the alignment of the spheres globally optimal spherical mixture alignment for camera pose estimation"
    - "eigendecomposition free training of deep networks with zero eigenvalue based losses"
    - "dynamic graph cnn for learning on point clouds"
    - "megadepth learning single view depth prediction from internet photos"
    - "geometric loss functions for camera pose regression with deep learning"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning to find good correspondences"

Connected through hyperedge HE-3 (induced by source paper "globally optimal consensus maximization for robust visual inertial localization in point and line map"):
  with members:
    - "a polynomial time solution for robust registration with extreme outlier rates"
    - "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"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-4 (induced by source paper "the apolloscape open dataset for autonomous driving and its application"):
  with members:
    - "deep stochastic attraction and repulsion embedding for image based localization"
    - "stochastic attraction repulsion embedding for large scale image localization"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deepmvs learning multi view stereopsis"
    - "apollocar3d a large 3d car instance understanding benchmark for autonomous driving"
    - "dels 3d deep localization and segmentation with a 3d semantic map"
    - "path aggregation network for instance segmentation"

Connected through hyperedge HE-5 (induced by source paper "ppfnet global context aware local features for robust 3d point matching"):
  with members:
    - "learning compact geometric features"
    - "cad priors for accurate and flexible instance reconstruction"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "geometric features for voxel based surface recognition"
    - "deep learning of local rgb d patches for 3d object detection and 6d pose estimation"
    - "learning to navigate the energy landscape"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

Connected through hyperedge HE-6 (induced by source paper "latent ransac"):
  with members:
    - "ansac adaptive non minimal sample and consensus"
    - "deep image homography estimation"
    - "posenet a convolutional network for real time 6 dof camera relocalization"

Connected through hyperedge HE-7 (induced by source paper "robust fitting in computer vision easy or hard"):
  with members:
    - "deterministic consensus maximization with biconvex programming"
    - "maximum consensus parameter estimation by reweighted ell_1 methods"
    - "the parameterized complexity of some geometric problems in unbounded dimension"

Connected through hyperedge HE-8 (induced by source paper "deterministic consensus maximization with biconvex programming"):
  with members:
    - "robust fitting in computer vision easy or hard"

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)