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 "automatic co registration of aerial imagery and untextured model data utilizing average shading gradients"):
  Abstract: The comparison of current image data with existing 3D model data of a scene provides an efficient method to keep models up to date. In order to transfer information between 2D and 3D data, a preliminary co-registration is necessary. In this paper, we present a concept to automatically co-register aerial imagery and untextured 3D model data. To refine a given initial camera pose, our algorithm computes dense correspondence fields using SIFT flow between gradient representations of the model and camera image, from which 2D–3D correspondences are obtained. These correspondences are then used in an iterative optimization scheme to refine the initial camera pose by minimizing the reprojection error. Since it is assumed that the model does not contain texture information, our algorithm is built up on an existing method based on Average Shading Gradients (ASG) to generate gradient images based on raw geometry information only. We apply our algorithm for the co-registering of aerial photographs to an untextured, noisy mesh model. We have investigated different magnitudes of input error and show that the proposed approach can reduce the final reprojection error to a minimum of 1.27 ± 0.54 pixels, which is less than 10% of its initial value. Furthermore, our evaluation shows that our approach outperforms the accuracy of a standard Iterative Closest Point (ICP) implementation.

Includes members:
  Member "learning less is more 6d camera localization via 3d surface regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visual camera re localization from rgb and rgb d images using dsac")
  Member "posenet a convolutional network for real time 6 dof camera relocalization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

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)