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: "rolling shutter camera relative pose generalized epipolar geometry"
  Abstract: The vast majority of modern consumer-grade cameras employ a rolling shutter mechanism. In dynamic geometric computer vision applications such as visual SLAM, the so-called rolling shutter effect therefore needs to be properly taken into account. A dedicated relative pose solver appears to be the first problem to solve, as it is of eminent importance to bootstrap any derivation of multi-view geometry. However, despite its significance, it has received inadequate attention to date. #R##N#This paper presents a detailed investigation of the geometry of the rolling shutter relative pose problem. We introduce the rolling shutter essential matrix, and establish its link to existing models such as the push-broom cameras, summarized in a clean hierarchy of multi-perspective cameras. The generalization of well-established concepts from epipolar geometry is completed by a definition of the Sampson distance in the rolling shutter case. The work is concluded with a careful investigation of the introduced epipolar geometry for rolling shutter cameras on several dedicated benchmarks.

Connected through hyperedge HE-1 (induced by source paper "rolling shutter aware differential sfm and image rectification"):
  with members:
    - "geometric models of rolling shutter cameras"

Connected through hyperedge HE-2 (induced by source paper "gyroscope aided relative pose estimation for rolling shutter cameras"):
  with members:
    - "self calibration based approach to critical motion sequences of rolling shutter structure from motion"

Connected through hyperedge HE-3 (induced by source paper "modeling varying camera imu time offset in optimization based visual inertial odometry"):
  with members:
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-4 (induced by source paper "direct sparse odometry with rolling shutter"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "direct sparse odometry"

Connected through hyperedge HE-5 (induced by source paper "joint stabilization and direction of 360 degrees videos"):
  with members:
    - "movie editing and cognitive event segmentation in virtual reality video"

Connected through hyperedge HE-6 (induced by source paper "inertial aided rolling shutter relative pose estimation"):
  with members:
    - "self calibration based approach to critical motion sequences of rolling shutter structure from motion"

Connected through hyperedge HE-7 (induced by source paper "image based camera localization an overview"):
  with members:
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "event based 6 dof camera tracking from photometric depth maps"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "geometric loss functions for camera pose regression with deep learning"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "robust stereo visual inertial odometry for fast autonomous flight"

Connected through hyperedge HE-8 (induced by source paper "self calibration based approach to critical motion sequences of rolling shutter structure from motion"):
  with members:
    - "geometric models of rolling shutter cameras"

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)