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 video capture using a learning based hybrid imaging system"
  Abstract: Light field cameras have many advantages over traditional cameras, as they allow the user to change various camera settings after capture. However, capturing light fields requires a huge bandwidth to record the data: a modern light field camera can only take three images per second. This prevents current consumer light field cameras from capturing light field videos. Temporal interpolation at such extreme scale (10x, from 3 fps to 30 fps) is infeasible as too much information will be entirely missing between adjacent frames. Instead, we develop a hybrid imaging system, adding another standard video camera to capture the temporal information. Given a 3 fps light field sequence and a standard 30 fps 2D video, our system can then generate a full light field video at 30 fps. We adopt a learning-based approach, which can be decomposed into two steps: spatio-temporal flow estimation and appearance estimation. The flow estimation propagates the angular information from the light field sequence to the 2D video, so we can warp input images to the target view. The appearance estimation then combines these warped images to output the final pixels. The whole process is trained end-to-end using convolutional neural networks. Experimental results demonstrate that our algorithm outperforms current video interpolation methods, enabling consumer light field videography, and making applications such as refocusing and parallax view generation achievable on videos for the first time.

Connected through hyperedge HE-1 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "deeply supervised salient object detection with short connections"
    - "synthetic depth of field with a single camera mobile phone"
    - "deeplens shallow depth of field from a single image"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "scanner efficient video analysis at scale"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-2 (induced by source paper "video enhancement with task oriented flow"):
  with members:
    - "frame recurrent video super resolution"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "multi frame quality enhancement for compressed video"
    - "context aware synthesis for video frame interpolation"
    - "flow guided feature aggregation for video object detection"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "accurate optical flow via direct cost volume processing"

Connected through hyperedge HE-3 (induced by source paper "5d light field synthesis from a monocular video"):
  with members:
    - "synthesizing a 4d spatio angular consistent light field from a single image"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "crossnet an end to end reference based super resolution network using cross scale warping"):
  with members:
    - "video enhancement with task oriented flow"
    - "frame recurrent video super resolution"
    - "deep view morphing"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "detail revealing deep video super resolution"
    - "video frame synthesis using deep voxel flow"

Connected through hyperedge HE-6 (induced by source paper "learning based video motion magnification"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "personalized cinemagraphs using semantic understanding and collaborative learning"
    - "decomposing motion and content for natural video sequence prediction"
    - "video acceleration magnification"
    - "video frame interpolation via adaptive convolution"
    - "video frame interpolation via adaptive separable convolution"
    - "video frame synthesis using deep voxel flow"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (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"
    - "geometry consistent light field super resolution via graph based regularization"
    - "enhanced deep residual networks for single image super resolution"
    - "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"

Connected through hyperedge HE-8 (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"
    - "occlusion model guided antiocclusion depth estimation in light field"
    - "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)