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: "occlusion model guided antiocclusion depth estimation in light field"
  Abstract: Occlusion is one of the most challenging problems in depth estimation. Previous work has modeled the single-occluder occlusion in light field and achieves good performances, however it is still difficult to obtain accurate depth for multioccluder occlusion. In this paper, we explore the complete occlusion model in light field and derive the occluder-consistency between the spatial and angular spaces, which is used as a guidance to select unoccluded views for each candidate occlusion point. Then, an antiocclusion energy function is built to regularize the depth map. Experimental results on both synthetic and real light-field datasets have demonstrated the advantages of the proposed algorithm compared with state-of-the-art algorithms of light-field depth estimation, especially in multioccluder cases.

Connected through hyperedge HE-1 (induced by source paper "depth map estimation for free viewpoint television"):
  with members:
    - "hole filling with multiple reference views in dibr view synthesis"
    - "single view and multi view depth fusion"

Connected through hyperedge HE-2 (induced by source paper "dense light field reconstruction from sparse sampling using residual network"):
  with members:
    - "a generic multi projection center model and calibration method for light field cameras"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "light field video capture using a learning based hybrid imaging system"
    - "learning based view synthesis for light field cameras"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

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)