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: "hierarchical structure and motion recovery from uncalibrated images"
  Abstract: We describe a hierarchical structure-from-motion pipeline.No information is needed beside images themselves.The pipeline proved successful in real-world tasks. This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D structure from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.

Connected through hyperedge HE-1 (induced by source paper "graph based parallel large scale structure from motion"):
  with members:
    - "baseline desensitizing in translation averaging"
    - "rotation averaging and strong duality"
    - "parallel structure from motion from local increment to global averaging"
    - "large scale sfm with the distributed camera model"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-2 (induced by source paper "batch based monocular slam for egocentric videos"):
  with members:
    - "computing egomotion with local loop closures for egocentric videos"
    - "compact cnn for indexing egocentric videos"
    - "egosampling fast forward and stereo for egocentric videos"
    - "predicting daily activities from egocentric images using deep learning"
    - "orb slam a versatile and accurate monocular slam system"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-3 (induced by source paper "robust sfm with little image overlap"):
  with members:
    - "on computing the translations norm in the epipolar graph"
    - "linear global translation estimation with feature tracks"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-4 (induced by source paper "parallel structure from motion from local increment to global averaging"):
  with members:
    - "large scale sfm with the distributed camera model"
    - "linear global translation estimation with feature tracks"
    - "robust camera location estimation by 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)