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: "the double sphere camera model"
  Abstract: Vision-based motion estimation and 3D reconstruction, which have numerous applications (e.g., autonomous driving, navigation systems for airborne devices and augmented reality) are receiving significant research attention. To increase the accuracy and robustness, several researchers have recently demonstrated the benefit of using large field-of-view cameras for such applications. In this paper, we provide an extensive review of existing models for large field-of-view cameras. For each model we provide projection and unprojection functions and the subspace of points that result in valid projection. Then, we propose the Double Sphere camera model that well fits with large field-of-view lenses, is computationally inexpensive and has a closed-form inverse. We evaluate the model using a calibration dataset with several different lenses and compare the models using the metrics that are relevant for Visual Odometry, i.e., reprojection error, as well as computation time for projection and unprojection functions and their Jacobians. We also provide qualitative results and discuss the performance of all models.

Connected through hyperedge HE-1 (induced by source paper "spherical formulation of moving object geometric constraints for monocular fisheye cameras"):
  with members:
    - "coherent motion segmentation in moving camera videos using optical flow orientations"

Connected through hyperedge HE-2 (induced by source paper "a mobile manipulation system for one shot teaching of complex tasks in homes"):
  with members:
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "visual inertial teach and repeat for aerial inspection"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "the devil is in the tails fine grained classification in the wild"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-3 (induced by source paper "versavis an open versatile multi camera visual inertial sensor suite"):
  with members:
    - "experimental comparison of visual aided odometry methods for rail vehicles"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "incremental object database building 3d models from multiple partial observations"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "pirvs an advanced visual inertial slam system with flexible sensor fusion and hardware co design"

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)