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: "on the two view geometry of unsynchronized cameras"
  Abstract: We present new methods for simultaneously estimating camera geometry and time shift from video sequences from multiple unsynchronized cameras. Algorithms for simultaneous computation of a fundamental matrix or a homography with unknown time shift between images are developed. Our methods use minimal correspondence sets (eight for fundamental matrix and four and a half for homography) and therefore are suitable for robust estimation using RANSAC. Furthermore, we present an iterative algorithm that extends the applicability on sequences which are significantly unsynchronized, finding the correct time shift up to several seconds. We evaluated the methods on synthetic and wide range of real world datasets and the results show a broad applicability to the problem of camera synchronization.

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:
    - "rolling shutter camera synchronization with sub millisecond accuracy"
    - "bop benchmark for 6d object pose estimation"
    - "3d human pose estimation in rgbd images for robotic task learning"
    - "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 "brno urban dataset the new data for self driving agents and mapping tasks"):
  with members:
    - "self driving cars a survey"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "advio an authentic dataset for visual inertial odometry"
    - "playing for benchmarks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "torontocity seeing the world with a million eyes"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-3 (induced by source paper "a sparse resultant based method for efficient minimal solvers"):
  with members:
    - "a general solver based on sparse resultants"

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)