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: "shapefit exact location recovery from corrupted pairwise directions"
  Abstract: Let $t_1,\ldots,t_n \in \mathbb{R}^d$ and consider the location recovery problem: given a subset of pairwise direction observations $\{(t_i - t_j) / \|t_i - t_j\|_2\}_{i<j \in [n] \times [n]}$, where a constant fraction of these observations are arbitrarily corrupted, find $\{t_i\}_{i=1}^n$ up to a global translation and scale. We propose a novel algorithm for the location recovery problem, which consists of a simple convex program over $dn$ real variables. We prove that this program recovers a set of $n$ i.i.d. Gaussian locations exactly and with high probability if the observations are given by an \erdosrenyi graph, $d$ is large enough, and provided that at most a constant fraction of observations involving any particular location are adversarially corrupted. We also prove that the program exactly recovers Gaussian locations for $d=3$ if the fraction of corrupted observations at each location is, up to poly-logarithmic factors, at most a constant. Both of these recovery theorems are based on a set of deterministic conditions that we prove are sufficient for exact recovery.

Connected through hyperedge HE-1 (induced by source paper "exact camera location recovery by least unsquared deviations"):
  with members:
    - "baseline desensitizing in translation averaging"
    - "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"
    - "robust computation of linear models by convex relaxation"
    - "robust camera location estimation by convex programming"
    - "stable camera motion estimation using convex programming"

Connected through hyperedge HE-2 (induced by source paper "estimation of camera locations in highly corrupted scenarios all about that base no shape trouble"):
  with members:
    - "exact camera location recovery by least unsquared deviations"
    - "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"
    - "robust camera location estimation by convex programming"
    - "stable camera motion estimation using convex programming"

Connected through hyperedge HE-3 (induced by source paper "a survey of structure from motion"):
  with members:
    - "a survey on non filter based monocular visual slam systems"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "exact simultaneous recovery of locations and structure from known orientations and corrupted point correspondences"
    - "robust inference for visual inertial sensor fusion"
    - "robust camera location estimation by convex programming"
    - "stable camera motion estimation using convex programming"
    - "exact and stable recovery of rotations for robust synchronization"
    - "eigenvector synchronization graph rigidity and the molecule problem"

Connected through hyperedge HE-4 (induced by source paper "exact simultaneous recovery of locations and structure from known orientations and corrupted point correspondences"):
  with members:
    - "shapefit and shapekick for robust scalable structure from motion"
    - "linear global translation estimation with feature tracks"
    - "stable camera motion estimation using convex programming"
    - "exact and stable recovery of rotations for robust synchronization"

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)