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 synchronization with sub millisecond accuracy"
  Abstract: A simple method for synchronization of video streams with a precision better than one millisecond is proposed. The method is applicable to any number of rolling shutter cameras and when a few photographic flashes or other abrupt lighting changes are present in the video. The approach exploits the rolling shutter sensor property that every sensor row starts its exposure with a small delay after the onset of the previous row. The cameras may have different frame rates and resolutions, and need not have overlapping fields of view. The method was validated on five minutes of four streams from an ice hockey match. The found transformation maps events visible in all cameras to a reference time with a standard deviation of the temporal error in the range of 0.3 to 0.5 milliseconds. The quality of the synchronization is demonstrated on temporally and spatially overlapping images of a fast moving puck observed in two cameras.

Connected through hyperedge HE-1 (induced by source paper "teaching robots to imitate a human with no on teacher sensors what are the key challenges"):
  with members:
    - "bop benchmark for 6d object pose estimation"
    - "3d human pose estimation in rgbd images for robotic task learning"
    - "on the two view geometry of unsynchronized cameras"
    - "towards accurate multi person pose estimation in the wild"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "mask r cnn"
    - "realtime multi person 2d pose estimation using part affinity fields"

Connected through hyperedge HE-2 (induced by source paper "wireless software synchronization of multiple distributed cameras"):
  with members:
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "camswarm instantaneous smartphone camera arrays for collaborative photography"
    - "panoswarm collaborative and synchronized multi device panoramic photography"
    - "3dunderworld sls an open source structured light scanning system for rapid geometry acquisition"

Connected through hyperedge HE-3 (induced by source paper "on the two view geometry of unsynchronized cameras"):
  with members:
    - "a clever elimination strategy for efficient minimal solvers"

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)