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: "robust fitting in computer vision easy or hard"
  Abstract: Robust model fitting plays a vital role in computer vision, and research into algorithms for robust fitting continues to be active. Arguably the most popular paradigm for robust fitting in computer vision is consensus maximisation, which strives to find the model parameters that maximise the number of inliers. Despite the significant developments in algorithms for consensus maximisation, there has been a lack of fundamental analysis of the problem in the computer vision literature. In particular, whether consensus maximisation is "tractable" remains a question that has not been rigorously dealt with, thus making it difficult to assess and compare the performance of proposed algorithms, relative to what is theoretically achievable. To shed light on these issues, we present several computational hardness results for consensus maximisation. Our results underline the fundamental intractability of the problem, and resolve several ambiguities existing in the literature.

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 "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"
    - "guaranteed outlier removal for point cloud registration with correspondences"
    - "rotation averaging and strong duality"
    - "sparse representation for 3d shape estimation a convex relaxation approach"

Connected through hyperedge HE-3 (induced by source paper "event based star tracking via multiresolution progressive hough transforms"):
  with members:
    - "empirical analysis of common subgraph isomorphism approaches to the lost in space star identification problem"
    - "a unifying contrast maximization framework for event cameras with applications to motion depth and optical flow estimation"
    - "star tracking using an event camera"
    - "rotation averaging and strong duality"
    - "real time panoramic tracking for event cameras"

Connected through hyperedge HE-4 (induced by source paper "a new method for atlanta world frame estimation"):
  with members:
    - "geometric multi model fitting with a convex relaxation algorithm"
    - "multi class model fitting by energy minimization and mode seeking"
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"
    - "robust and globally optimal manhattan frame estimation in near real time"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-5 (induced by source paper "a novel method for the absolute pose problem with pairwise constraints"):
  with members:
    - "deterministic consensus maximization with biconvex programming"
    - "inloc indoor visual localization with dense matching and view synthesis"
    - "an efficient algebraic solution to the perspective three point problem"
    - "efficient global point cloud alignment using bayesian nonparametric mixtures"
    - "go icp a globally optimal solution to 3d icp point set registration"

Connected through hyperedge HE-6 (induced by source paper "convex relaxations for consensus and non minimal problems in 3d vision"):
  with members:
    - "modeling perceptual aliasing in slam via discrete continuous graphical models"
    - "deterministic consensus maximization with biconvex programming"
    - "rotation averaging and strong duality"
    - "a minimal solution for two view focal length estimation using two affine correspondences"

Connected through hyperedge HE-7 (induced by source paper "consensus maximization tree search revisited"):
  with members:
    - "deterministic consensus maximization with biconvex programming"
    - "maximum consensus parameter estimation by reweighted ell_1 methods"
    - "optimal point placement for mesh smoothing"

Connected through hyperedge HE-8 (induced by source paper "model free consensus maximization for non rigid shapes"):
  with members:
    - "deep functional maps structured prediction for dense shape correspondence"
    - "building statistical shape spaces for 3d human modeling"
    - "product manifold filter non rigid shape correspondence via kernel density estimation in the product space"
    - "a combinatorial solution to non rigid 3d shape to image matching"
    - "volumedeform real time volumetric non rigid reconstruction"
    - "mlpnp a real time maximum likelihood solution to the perspective n point problem"
    - "template based monocular 3d shape recovery using laplacian meshes"
    - "spectral generalized multi dimensional scaling"

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)