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: "deterministic consensus maximization with biconvex programming"
  Abstract: Consensus maximization is one of the most widely used robust fitting paradigms in computer vision, and the development of algorithms for consensus maximization is an active research topic. In this paper, we propose an efficient deterministic optimization algorithm for consensus maximization. Given an initial solution, our method conducts a deterministic search that forcibly increases the consensus of the initial solution. We show how each iteration of the update can be formulated as an instance of biconvex programming, which we solve efficiently using a novel biconvex optimization algorithm. In contrast to our algorithm, previous consensus improvement techniques rely on random sampling or relaxations of the objective function, which reduce their ability to significantly improve the initial consensus. In fact, on challenging instances, the previous techniques may even return a worse off solution. Comprehensive experiments show that our algorithm can consistently and greatly improve the quality of the initial solution, without substantial cost.

Connected through hyperedge HE-1 (induced by source paper "glimps a greedy mixed integer approach for super robust matched subspace detection"):
  with members:
    - "deterministic hypothesis generation for robust fitting of multiple structures"
    - "dual principal component pursuit"
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "coherent motion segmentation in moving camera videos using optical flow orientations"
    - "high rank matrix completion and subspace clustering with missing data"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "high dimensional matched subspace detection when data are missing"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-2 (induced by source paper "sdrsac semidefinite based randomized approach for robust point cloud registration without correspondences"):
  with members:
    - "on device scalable image based localization via prioritized cascade search and fast one many ransac"
    - "maximum consensus parameter estimation by reweighted ell_1 methods"
    - "ds a flexible scalable and provably tight relaxation for matching problems"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "alternating direction graph matching"
    - "outlier robust icp for minimizing fractional rmsd"

Connected through hyperedge HE-3 (induced by source paper "a novel method for the absolute pose problem with pairwise constraints"):
  with members:
    - "robust fitting in computer vision easy or hard"
    - "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-4 (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"
    - "robust fitting in computer vision easy or hard"
    - "rotation averaging and strong duality"
    - "a minimal solution for two view focal length estimation using two affine correspondences"

Connected through hyperedge HE-5 (induced by source paper "consensus maximization tree search revisited"):
  with members:
    - "robust fitting in computer vision easy or hard"
    - "maximum consensus parameter estimation by reweighted ell_1 methods"
    - "optimal point placement for mesh smoothing"

Connected through hyperedge HE-6 (induced by source paper "an efficient solution to non minimal case essential matrix estimation"):
  with members:
    - "equivalent constraints for two view geometry pose solution pure rotation identification and 3d reconstruction"

Connected through hyperedge HE-7 (induced by source paper "robust fitting in computer vision easy or hard"):
  with members:
    - "maximum consensus parameter estimation by reweighted ell_1 methods"
    - "globally optimal inlier set maximisation for simultaneous camera pose and feature correspondence"
    - "the parameterized complexity of some geometric problems in unbounded dimension"

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)