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 lightweight optical flow cnn revisiting data fidelity and regularization"
  Abstract: Over four decades, the majority addresses the problem of optical flow estimation using variational methods. With the advance of machine learning, some recent works have attempted to address the problem using convolutional neural network (CNN) and have showed promising results. FlowNet2, the state-of-the-art CNN, requires over 160M parameters to achieve accurate flow estimation. Our LiteFlowNet2 outperforms FlowNet2 on Sintel and KITTI benchmarks, while being 25.3 times smaller in the model size and 3.1 times faster in the running speed. LiteFlowNet2 is built on the foundation laid by conventional methods and resembles the corresponding roles as data fidelity and regularization in variational methods. We compute optical flow in a spatial-pyramid formulation as SPyNet but through a novel lightweight cascaded flow inference. It provides high flow estimation accuracy through early correction with seamless incorporation of descriptor matching. Flow regularization is used to ameliorate the issue of outliers and vague flow boundaries through feature-driven local convolutions. Our network also owns an effective structure for pyramidal feature extraction and embraces feature warping rather than image warping as practiced in FlowNet2 and SPyNet. Comparing to LiteFlowNet, LiteFlowNet2 improves the optical flow accuracy on Sintel Clean by 23.3%, Sintel Final by 12.8%, KITTI 2012 by 19.6%, and KITTI 2015 by 18.8%, while being 2.2 times faster. Our network protocol and trained models are made publicly available on this https URL.

Connected through hyperedge HE-1 (induced by source paper "scopeflow dynamic scene scoping for optical flow"):
  with members:
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "selflow self supervised learning of optical flow"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a fusion approach for multi frame optical flow estimation"
    - "dgc net dense geometric correspondence network"
    - "proflow learning to predict optical flow"

Connected through hyperedge HE-2 (induced by source paper "glu net global local universal network for dense flow and correspondences"):
  with members:
    - "geometric image correspondence verification by dense pixel matching"
    - "dynamic context correspondence network for semantic alignment"
    - "an empirical evaluation study on the training of sdc features for dense pixel matching"
    - "glampoints greedily learned accurate match points"
    - "semantic attribute matching networks"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "lf net learning local features from images"
    - "models matter so does training an empirical study of cnns for optical flow estimation"

Connected through hyperedge HE-3 (induced by source paper "boosting real time driving scene parsing with shared semantics"):
  with members:
    - "short term prediction and multi camera fusion on semantic grids"
    - "sfnet learning object aware semantic correspondence"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "learning correspondence from the cycle consistency of time"
    - "searching for mobilenetv3"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "dynamic video segmentation network"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-4 (induced by source paper "edvr video restoration with enhanced deformable convolutional networks"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "path restore learning network path selection for image restoration"
    - "video enhancement with task oriented flow"
    - "frame recurrent video super resolution"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "object detection in video with spatiotemporal sampling networks"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "scale recurrent network for deep image deblurring"

Connected through hyperedge HE-5 (induced by source paper "the five elements of flow"):
  with members:
    - "sense a shared encoder network for scene flow estimation"
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "selflow self supervised learning of optical flow"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a fusion approach for multi frame optical flow estimation"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "hierarchical discrete distribution decomposition for match density estimation"

Connected through hyperedge HE-6 (induced by source paper "deep flow guided video inpainting"):
  with members:
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "youtube vos a large scale video object segmentation benchmark"
    - "generative image inpainting with contextual attention"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "context encoders feature learning by inpainting"
    - "deep residual learning for image recognition"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

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)