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: "resultant based incremental recovery of camera pose from pairwise matches"
  Abstract: Incremental (online) structure from motion pipelines seek to recover the camera matrix associated with an image $I_n$ given $n-1$ images, $I_1,...,I_{n-1}$, whose camera matrices have already been recovered. In this paper, we introduce a novel solution to the six-point online algorithm to recover the exterior parameters associated with $I_n$. Our algorithm uses just six corresponding pairs of 2D points, extracted each from $I_n$ and from \textit{any} of the preceding $n-1$ images, allowing the recovery of the full six degrees of freedom of the $n$'th camera, and unlike common methods, does not require tracking feature points in three or more images. Our novel solution is based on constructing a Dixon resultant, yielding a solution method that is both efficient and accurate compared to existing solutions. We further use Bernstein's theorem to prove a tight bound on the number of complex solutions. Our experiments demonstrate the utility of our approach.

Connected through hyperedge HE-1 (induced by source paper "video object segmentation based visual servo control and object depth estimation on a mobile robot platform"):
  with members:
    - "bubblenets learning to select the guidance frame in video object segmentation by deep sorting frames"
    - "self supervised robot in hand object learning"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "fast and accurate online video object segmentation via tracking parts"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"
    - "sim to real via sim to sim data efficient robotic grasping via randomized to canonical adaptation networks"
    - "craves controlling robotic arm with a vision based economic system"

Connected through hyperedge HE-2 (induced by source paper "trifocal relative pose from lines at points and its efficient solution"):
  with members:
    - "stereo relative pose from line and point feature triplets"
    - "beyond gr obner bases basis selection for minimal solvers"
    - "joint spatio temporal boundary detection and boundary flow prediction with a fully convolutional siamese network"
    - "a line point unified solution to relative camera pose estimation"
    - "on some properties of calibrated trifocal tensors"
    - "from multiview image curves to 3d drawings"
    - "multiview differential geometry of curves"

Connected through hyperedge HE-3 (induced by source paper "averaging essential and fundamental matrices in collinear camera settings"):
  with members:
    - "algebraic characterization of essential matrices and their averaging in multiview settings"
    - "on the solvability of viewing graphs"
    - "gpsfm global projective sfm using algebraic constraints on multi view fundamental matrices"
    - "a new rank constraint on multi view fundamental matrices and its application to camera location recovery"
    - "linear global translation estimation with feature tracks"
    - "robust camera location estimation by convex programming"

Connected through hyperedge HE-4 (induced by source paper "algebraic characterization of essential matrices and their averaging in multiview settings"):
  with members:
    - "gpsfm global projective sfm using algebraic constraints on multi view fundamental matrices"
    - "rotation averaging and strong duality"
    - "a new rank constraint on multi view fundamental matrices and its application to camera location recovery"
    - "shapefit and shapekick for robust scalable structure from motion"
    - "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)