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 "averaging essential and fundamental matrices in collinear camera settings"):
  Abstract: Global methods to Structure from Motion have gained popularity in recent years. A significant drawback of global methods is their sensitivity to collinear camera settings. In this paper, we introduce an analysis and algorithms for averaging bifocal tensors (essential or fundamental matrices) when either subsets or all of the camera centers are collinear. #R##N#We provide a complete spectral characterization of bifocal tensors in collinear scenarios and further propose two averaging algorithms. The first algorithm uses rank constrained minimization to recover camera matrices in fully collinear settings. The second algorithm enriches the set of possibly mixed collinear and non-collinear cameras with additional, "virtual cameras," which are placed in general position, enabling the application of existing averaging methods to the enriched set of bifocal tensors. Our algorithms are shown to achieve state of the art results on various benchmarks that include autonomous car datasets and unordered image collections in both calibrated and unclibrated settings.

Includes members:
  Member "resultant based incremental recovery of camera pose from pairwise matches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "video object segmentation based visual servo control and object depth estimation on a mobile robot platform")
  Member "algebraic characterization of essential matrices and their averaging in multiview settings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "necessary and sufficient polynomial constraints on compatible triplets of essential matrices")
  Member "on the solvability of viewing graphs":
    also connected through other hyperedges:
      (none)
  Member "gpsfm global projective sfm using algebraic constraints on multi view fundamental matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "algebraic characterization of essential matrices and their averaging in multiview settings")
  Member "a new rank constraint on multi view fundamental matrices and its application to camera location recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "algebraic characterization of essential matrices and their averaging in multiview settings")
  Member "linear global translation estimation with feature tracks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "algebraic characterization of essential matrices and their averaging in multiview settings")
  Member "robust camera location estimation by convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning transformation synchronization")

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)