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: "magsac marginalizing sample consensus"
  Abstract: A method called sigma-consensus is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC. Instead of estimating sigma, it is marginalized over a range of noise scales using a Bayesian estimator, i.e. the optimized model is obtained as the weighted average using the posterior probabilities as weights. Applying sigma-consensus, two methods are proposed: (i) a post-processing step which always improved the model quality on a wide range of vision problems without noticeable deterioration in processing time, i.e. at most 1-2 milliseconds; and (ii) a locally optimized RANSAC, called LO-MAGSAC, which includes sigma-consensus to the local optimization of LO-RANSAC. The method is superior to the state-of-the-art in terms of geometric accuracy on publicly available real world datasets for epipolar geometry (F and E), homography and affine transformation estimation.

Connected through hyperedge HE-1 (induced by source paper "magsac a fast reliable and accurate robust estimator"):
  with members:
    - "an evaluation of feature matchers forfundamental matrix estimation"
    - "graph cut ransac"
    - "mods fast and robust method for two view matching"

Connected through hyperedge HE-2 (induced by source paper "progressive napsac sampling from gradually growing neighborhoods"):
  with members:
    - "graph cut ransac"
    - "mods fast and robust method for two view matching"

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)