human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "baseline desensitizing in translation averaging"
  Abstract: Many existing translation averaging algorithms are either sensitive to disparate camera baselines and have to rely on extensive preprocessing to improve the observed Epipolar Geometry graph, or if they are robust against disparate camera baselines, require complicated optimization to minimize the highly nonlinear angular error objective. In this paper, we carefully design a simple yet effective bilinear objective function, introducing a variable to perform the requisite normalization. The objective function enjoys the baseline-insensitive property of the angular error and yet is amenable to simple and efficient optimization by block coordinate descent, with good empirical performance. A rotation-assisted Iterative Reweighted Least Squares scheme is further put forth to help deal with outliers. We also contribute towards a better understanding of the behavior of two recent convex algorithms, LUD and Shapefit/kick, clarifying the underlying subtle difference that leads to the performance gap. Finally, we demonstrate that our algorithm achieves overall superior accuracies in benchmark dataset compared to state-of-theart methods, and is also several times faster.

Connected through hyperedge HE-1 (induced by source paper "degeneracy in self calibration revisited and a deep learning solution for uncalibrated slam"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "learning 2d to 3d lifting for object detection in 3d for autonomous vehicles"
    - "rolling shutter aware differential sfm and image rectification"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "deep supervision with intermediate concepts"
    - "fisheyerecnet a multi context collaborative deep network for fisheye image rectification"
    - "challenges in monocular visual odometry photometric calibration motion bias and rolling shutter effect"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-2 (induced by source paper "graph based parallel large scale structure from motion"):
  with members:
    - "rotation averaging and strong duality"
    - "parallel structure from motion from local increment to global averaging"
    - "large scale sfm with the distributed camera model"
    - "hierarchical structure and motion recovery from uncalibrated images"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-3 (induced by source paper "exact camera location recovery by least unsquared deviations"):
  with members:
    - "estimation of camera locations in highly corrupted scenarios all about that base no shape trouble"
    - "a new rank constraint on multi view fundamental matrices and its application to camera location recovery"
    - "a survey of structure from motion"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "shapefit exact location recovery from corrupted pairwise directions"
    - "robust computation of linear models by convex relaxation"
    - "robust camera location estimation by convex programming"
    - "stable camera motion estimation using convex programming"

Question: Please tell me which class the center node 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)