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 "graph based parallel large scale structure from motion"):
  Abstract: While Structure from Motion (SfM) achieves great success in 3D reconstruction, it still meets challenges on large scale scenes. In this work, large scale SfM is deemed as a graph problem, and we tackle it in a divide-and-conquer manner. Firstly, the images clustering algorithm divides images into clusters with strong connectivity, leading to robust local reconstructions. Then followed with an image expansion step, the connection and completeness of scenes are enhanced by expanding along with a maximum spanning tree. After local reconstructions, we construct a minimum spanning tree (MinST) to find accurate similarity transformations. Then the MinST is transformed into a Minimum Height Tree (MHT) to find a proper anchor node and is further utilized to prevent error accumulation. When evaluated on different kinds of datasets, our approach shows superiority over the state-of-the-art in accuracy and efficiency. Our algorithm is open-sourced at this https URL.

Includes members:
  Member "baseline desensitizing in translation averaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "degeneracy in self calibration revisited and a deep learning solution for uncalibrated slam")
  Member "rotation averaging and strong duality":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the distribution of minima in intrinsic metric rotation averaging")
  Member "parallel structure from motion from local increment to global averaging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")
  Member "large scale sfm with the distributed camera model":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient 2d 3d matching for multi camera visual localization")
  Member "hierarchical structure and motion recovery from uncalibrated images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "batch based monocular slam for egocentric videos")
  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)