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 "a survey of structure from motion"):
  Abstract: The structure from motion (SfM) problem in computer vision is the problem of recovering the three-dimensional ($3$D) structure of a stationary scene from a set of projective measurements, represented as a collection of two-dimensional ($2$D) images, via estimation of motion of the cameras corresponding to these images. In essence, SfM involves the three main stages of (1) extraction of features in images (e.g., points of interest, lines, etc.) and matching these features between images, (2) camera motion estimation (e.g., using relative pairwise camera positions estimated from the extracted features), and (3) recovery of the $3$D structure using the estimated motion and features (e.g., by minimizing the so-called reprojection error). This survey mainly focuses on relatively recent developments in the literature pertaining to stages (2) and (3). More specifically, after touching upon the early factorization-based techniques for motion and structure estimation, we provide a detailed account of some of the recent camera location estimation methods in the literature, followed by discussion of notable techniques for $3$D structure recovery. We also cover the basics of the simultaneous localization and mapping (SLAM) problem, which can be viewed as a specific case of the SfM problem. Further, our survey includes a review of the fundamentals of feature extraction and matching (i.e., stage (1) above), various recent methods for handling ambiguities in $3$D scenes, SfM techniques involving relatively uncommon camera models and image features, and popular sources of data and SfM software.

Includes members:
  Member "a survey on non filter based monocular visual slam systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "monocular trail detection and tracking aided by visual slam for small unmanned aerial vehicles")
  Member "shapefit and shapekick for robust scalable structure from motion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "algebraic characterization of essential matrices and their averaging in multiview settings")
  Member "shapefit exact location recovery from corrupted pairwise directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exact camera location recovery by least unsquared deviations")
  Member "exact simultaneous recovery of locations and structure from known orientations and corrupted point correspondences":
    also connected through other hyperedges:
      (none)
  Member "robust inference for visual inertial sensor fusion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "visual inertial odometry of aerial robots")
  Member "robust camera location estimation by convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning transformation synchronization")
  Member "stable camera motion estimation using convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algebraic representations for volumetric frame fields")
  Member "exact and stable recovery of rotations for robust synchronization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the distribution of minima in intrinsic metric rotation averaging")

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)