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: "light field reconstruction using shearlet transform"
  Abstract: In this article we develop an image based rendering technique based on light field reconstruction from a limited set of perspective views acquired by cameras. Our approach utilizes sparse representation of epipolar-plane images in a directionally sensitive transform domain, obtained by an adapted discrete shearlet transform. The used iterative thresholding algorithm provides high-quality reconstruction results for relatively big disparities between neighboring views. The generated densely sampled light field of a given 3D scene is thus suitable for all applications which requires light field reconstruction. The proposed algorithm is compared favorably against state of the art depth image based rendering techniques.

Connected through hyperedge HE-1 (induced by source paper "deepview view synthesis with learned gradient descent"):
  with members:
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "stereo magnification learning view synthesis using multiplane images"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "photographic image synthesis with cascaded refinement networks"
    - "learning to learn by gradient descent by gradient descent"
    - "training deep nets with sublinear memory cost"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "lapepi net a laplacian pyramid epi structure for learning based dense light field reconstruction"):
  with members:
    - "a system for acquiring processing and rendering panoramic light field stills for virtual reality"
    - "crossnet an end to end reference based super resolution network using cross scale warping"
    - "stereo magnification learning view synthesis using multiplane images"
    - "learning based view synthesis for light field cameras"
    - "deepstereo learning to predict new views from the world s imagery"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "breaking the spatio angular trade off for light field super resolution via lstm modelling on epipolar plane images"):
  with members:
    - "stereo magnification learning view synthesis using multiplane images"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "learning based view synthesis for light field cameras"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-4 (induced by source paper "a fourier disparity layer representation for light fields"):
  with members:
    - "learning based view synthesis for light field cameras"
    - "partial light field tomographic reconstruction from a fixed camera focal stack"

Connected through hyperedge HE-5 (induced by source paper "surface light field compression using a point cloud codec"):
  with members:
    - "pseudo sequence based 2 d hierarchical coding structure for light field image compression"

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)