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: "a 4d light field dataset and cnn architectures for material recognition"
  Abstract: We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field. Our dataset contains 12 material categories, each with 100 images taken with a Lytro Illum, from which we extract about 30,000 patches in total. To the best of our knowledge, this is the first mid-size dataset for light-field images. Our main goal is to investigate whether the additional information in a light-field (such as multiple sub-aperture views and view-dependent reflectance effects) can aid material recognition. Since recognition networks have not been trained on 4D images before, we propose and compare several novel CNN architectures to train on light-field images. In our experiments, the best performing CNN architecture achieves a 7% boost compared with 2D image classification (70% to 77%). These results constitute important baselines that can spur further research in the use of CNNs for light-field applications. Upon publication, our dataset also enables other novel applications of light-fields, including object detection, image segmentation and view interpolation.

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"
    - "accurate light field depth estimation with superpixel regularization over partially occluded regions"
    - "characterizing and improving stability in neural style transfer"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-2 (induced by source paper "bonn activity maps dataset description"):
  with members:
    - "efficient 3d reconstruction and streaming for group scale multi client live telepresence"
    - "a dataset of multi illumination images in the wild"
    - "the replica dataset a digital replica of indoor spaces"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "latticenet fast point cloud segmentation using permutohedral lattices"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-3 (induced by source paper "lit light field inference of transparency for refractive object localization"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "glassloc plenoptic grasp pose detection in transparent clutter"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "grip generative robust inference and perception for semantic robot manipulation in adversarial environments"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "robust 6d object pose estimation with stochastic congruent sets"
    - "segmentation driven 6d object pose estimation"

Connected through hyperedge HE-4 (induced by source paper "light field saliency detection with deep convolutional networks"):
  with members:
    - "salient object detection by lossless feature reflection"
    - "a double deep spatio angular learning framework for light field based face recognition"
    - "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "deep contrast learning for salient object detection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "learning based view synthesis for light field cameras"

Connected through hyperedge HE-5 (induced by source paper "learning high fidelity light field images from hybrid inputs"):
  with members:
    - "deep learning for image super resolution a survey"
    - "geometry consistent light field super resolution via graph based regularization"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "stratified labeling for surface consistent parallax correction and occlusion completion"):
  with members:
    - "accurate light field depth estimation with superpixel regularization over partially occluded regions"
    - "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"
    - "semantic image inpainting with deep generative models"
    - "learning based view synthesis for light field cameras"

Connected through hyperedge HE-7 (induced by source paper "from bow to cnn two decades of texture representation for texture classification"):
  with members:
    - "deep learning for generic object detection a survey"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "low rank bilinear pooling for fine grained classification"
    - "deep ten texture encoding network"
    - "approximate fisher kernels of non iid image models for image categorization"
    - "using filter banks in convolutional neural networks for texture classification"

Connected through hyperedge HE-8 (induced by source paper "deep texture manifold for ground terrain recognition"):
  with members:
    - "island loss for learning discriminative features in facial expression recognition"
    - "exemplar centered supervised shallow parametric data embedding"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "hidden two stream convolutional networks for action recognition"
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "a recurrent encoder decoder network for sequential face alignment"
    - "deep ten texture encoding network"
    - "depth2action exploring embedded depth for large scale action recognition"

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)