human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "homography from two orientation and scale covariant features"):
  Abstract: This paper proposes a geometric interpretation of the angles and scales which the orientation- and scale-covariant feature detectors, e.g. SIFT, provide. Two new general constraints are derived on the scales and rotations which can be used in any geometric model estimation tasks. Using these formulas, two new constraints on homography estimation are introduced. Exploiting the derived equations, a solver for estimating the homography from the minimal number of two correspondences is proposed. Also, it is shown how the normalization of the point correspondences affects the rotation and scale parameters, thus achieving numerically stable results. Due to requiring merely two feature pairs, robust estimators, e.g. RANSAC, do significantly fewer iterations than by using the four-point algorithm. When using covariant features, e.g. SIFT, the information about the scale and orientation is given at no cost. The proposed homography estimation method is tested in a synthetic environment and on publicly available real-world datasets.

Includes members:
  Member "five point fundamental matrix estimation for uncalibrated cameras":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pl _ 1 p point line minimal problems under partial visibility in three views")
  Member "a clever elimination strategy for efficient minimal solvers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pl _ 1 p point line minimal problems under partial visibility in three views")
  Member "a minimal solution for two view focal length estimation using two affine correspondences":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pl _ 1 p point line minimal problems under partial visibility in three views")
  Member "radially distorted conjugate translations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "minimal solvers for indoor uav positioning")
  Member "graph cut ransac":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image matching across wide baselines from paper to practice")
  Member "mods fast and robust method for two view matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image matching across wide baselines from paper to practice")

Question: Please tell me which class the source paper 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)