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: "geometry consistent light field super resolution via graph based regularization"
  Abstract: Light field cameras capture the 3D information in a scene with a single exposure. This special feature makes light field cameras very appealing for a variety of applications: from post-capture refocus to depth estimation and image-based rendering. However, light field cameras suffer by design from strong limitations in their spatial resolution. Off-the-shelf super-resolution algorithms are not ideal for light field data, as they do not consider its structure. On the other hand, the few super-resolution algorithms explicitly tailored for light field data exhibit significant limitations, such as the need to carry out a costly disparity estimation procedure with sub-pixel precision. We propose a new light field super-resolution algorithm meant to address these limitations. We use the complementary information in the different light field views to augment the spatial resolution of the whole light field at once. In particular, we show that coupling the multi-view approach with a graph-based regularizer, which enforces the light field geometric structure, permits to avoid the need of a precise and costly disparity estimation step. Extensive experiments show that the new algorithm compares favorably to the state-of-the-art methods for light field super-resolution, both in terms of visual quality and in terms of reconstruction error.

Connected through hyperedge HE-1 (induced by source paper "learning high fidelity light field images from hybrid inputs"):
  with members:
    - "deep learning for image super resolution a survey"
    - "residual dense network for image super resolution"
    - "crossnet an end to end reference based super resolution network using cross scale warping"
    - "light field denoising via anisotropic parallax analysis in a cnn framework"
    - "light field blind motion deblurring"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "a 4d light field dataset and cnn architectures for material recognition"

Connected through hyperedge HE-2 (induced by source paper "a simple framework to leverage state of the art single image super resolution methods to restore light fields"):
  with members:
    - "spatial and angular resolution enhancement of light fields using convolutional neural networks"
    - "enhanced deep residual networks for single image super resolution"
    - "light field video capture using a learning based hybrid imaging system"
    - "beyond deep residual learning for image restoration persistent homology guided manifold simplification"
    - "geometry aware neighborhood search for learning local models for image superresolution"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "accurate image super resolution using very deep convolutional networks"

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)