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: "accurate light field depth estimation with superpixel regularization over partially occluded regions"
  Abstract: Depth estimation is a fundamental problem for light field photography applications. Numerous methods have been proposed in recent years, which either focus on crafting cost terms for more robust matching, or on analyzing the geometry of scene structures embedded in the epipolar-plane images. Significant improvements have been made in terms of overall depth estimation error; however, current state-of-the-art methods still show limitations in handling intricate occluding structures and complex scenes with multiple occlusions. To address these challenging issues, we propose a very effective depth estimation framework which focuses on regularizing the initial label confidence map and edge strength weights. Specifically, we first detect partially occluded boundary regions (POBR) via superpixel-based regularization. Series of shrinkage/reinforcement operations are then applied on the label confidence map and edge strength weights over the POBR. We show that after weight manipulations, even a low-complexity weighted least squares model can produce much better depth estimation than the state-of-the-art methods in terms of average disparity error rate, occlusion boundary precision-recall rate, and the preservation of intricate visual features.

Connected through hyperedge HE-1 (induced by source paper "style transfer for light field photography"):
  with members:
    - "a content transformation block for image style transfer"
    - "style transfer by relaxed optimal transport and self similarity"
    - "artistic style transfer for videos and spherical images"
    - "learning linear transformations for fast arbitrary style transfer"
    - "stereoscopic neural style transfer"
    - "characterizing and improving stability in neural style transfer"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "universal style transfer via feature transforms"

Connected through hyperedge HE-2 (induced by source paper "stratified labeling for surface consistent parallax correction and occlusion completion"):
  with members:
    - "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"
    - "stereo magnification learning view synthesis using multiplane images"
    - "light field video capture using a learning based hybrid imaging system"
    - "towards a quality metric for dense light fields"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "semantic image inpainting with deep generative models"
    - "learning based view synthesis for light field cameras"

Connected through hyperedge HE-3 (induced by source paper "dynamic spectral residual superpixels"):
  with members:
    - "superpixel contracted graph based learning for hyperspectral image classification"
    - "superpixel based color transfer"
    - "selflow self supervised learning of optical flow"
    - "superpixels an evaluation of the state of the art"
    - "peekaboo where are the objects structure adjusting superpixels"
    - "deep contrast learning for salient object detection"
    - "deep convolutional neural fields for depth estimation from a single image"

Connected through hyperedge HE-4 (induced by source paper "light field denoising via anisotropic parallax analysis in a cnn framework"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a 4d light field dataset and cnn architectures for material recognition"
    - "learning based view synthesis for light field cameras"
    - "you only look once unified real time object detection"
    - "accurate image super resolution using very deep convolutional networks"
    - "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)