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: "two points fundamental matrix"
  Abstract: It is well known that computing the fundamental matrix of two uncalibrated cameras requires at least seven corresponding points. We present a method to compute the fundamental matrix between two cameras with only two pairs of corresponding points. Given these two points, we show how to find three pairs of corresponding epipolar lines, from which the fundamental matrix can be computed. #R##N#Two pairs of epipolar lines, incident to the two pairs of corresponding points, are found by maximizing stereo consistency between lines; corresponding epipolar lines yield a good stereo correspondence. These two epipolar lines intersect at the epipoles, giving a third pair of corresponding points. A third pair of matching epipolar lines, needed to compute the fundamental matrix, is found from lines incident to the epipoles. #R##N#We validate our method using real-world images and compare it to state-of-the-art methods. Our approach is more accurate by a factor of five compared to the standard method using seven corresponding points, and its accuracy is comparable to the 8-points algorithm.

Connected through hyperedge HE-1 (induced by source paper "nonnegative matrix underapproximation for robust multiple model fitting"):
  with members:
    - "fast l1 nmf for multiple parametric model estimation"
    - "compressed nonnegative matrix factorization is fast and accurate"
    - "a biclustering framework for consensus problems"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "on the complexity of nonnegative matrix factorization"

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)