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: "fundamental matrix estimation a study of error criteria"
  Abstract: The fundamental matrix (FM) describes the geometric relations that exist between two images of the same scene. Different error criteria are used for estimating FMs from an input set of correspondences. In this paper, the accuracy and efficiency aspects of the different error criteria are studied. We mathematically and experimentally proved that the most popular error criterion, the symmetric epipolar distance, is biased. It was also shown that despite the similarity between the algebraic expressions of the symmetric epipolar distance and Sampson distance, they have different accuracy properties. In addition, a new error criterion, Kanatani distance, was proposed and proved to be the most effective for use during the outlier removal phase from accuracy and efficiency perspectives. To thoroughly test the accuracy of the different error criteria, we proposed a randomized algorithm for Reprojection Error-based Correspondence Generation (RE-CG). As input, RE-CG takes an FM and a desired reprojection error value d. As output, RE-CG generates a random correspondence having that error value. Mathematical analysis of this algorithm revealed that the success probability for any given trial is 1-(2/3)^2 at best and is 1-(6/7)^2 at worst while experiments demonstrated that the algorithm often succeeds after only one trial.

Connected through hyperedge HE-1 (induced by source paper "an evaluation of feature matchers forfundamental matrix estimation"):
  with members:
    - "geodesc learning local descriptors by integrating geometry constraints"
    - "repeatability is not enough learning affine regions via discriminability"
    - "hpatches a benchmark and evaluation of handcrafted and learned local descriptors"
    - "learning to find good correspondences"
    - "working hard to know your neighbor s margins local descriptor learning loss"
    - "graph cut ransac"
    - "orb slam a versatile and accurate monocular slam system"
    - "domain size pooling in local descriptors dsp sift"

Connected through hyperedge HE-2 (induced by source paper "deep fundamental matrix estimation without correspondences"):
  with members:
    - "vision based real estate price estimation"
    - "superpoint self supervised interest point detection and description"
    - "camera relocalization by predicting pairwise relative poses using convolutional neural network"
    - "convolutional neural network architecture for geometric matching"
    - "unsupervised deep homography a fast and robust homography estimation model"
    - "toward geometric deep slam"
    - "surfacenet an end to end 3d neural network for multiview stereopsis"
    - "deep 6 dof tracking"

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)