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: "liteflownet a lightweight convolutional neural network for optical flow estimation"
  Abstract: FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow estimation, requires over 160M parameters to achieve accurate flow estimation. In this paper we present an alternative network that outperforms FlowNet2 on the challenging Sintel final pass and KITTI benchmarks, while being 30 times smaller in the model size and 1.36 times faster in the running speed. This is made possible by drilling down to architectural details that might have been missed in the current frameworks: (1) We present a more effective flow inference approach at each pyramid level through a lightweight cascaded network. It not only improves flow estimation accuracy through early correction, but also permits seamless incorporation of descriptor matching in our network. (2) We present a novel flow regularization layer to ameliorate the issue of outliers and vague flow boundaries by using a feature-driven local convolution. (3) Our network owns an effective structure for pyramidal feature extraction and embraces feature warping rather than image warping as practiced in FlowNet2. Our code and trained models are available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "softmax splatting for video frame interpolation"):
  with members:
    - "unsupervised video interpolation using cycle consistency"
    - "depth aware video frame interpolation"
    - "video enhancement with task oriented flow"
    - "phasenet for video frame interpolation"
    - "temporal interpolation as an unsupervised pretraining task for optical flow estimation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning to synthesize motion blur"
    - "deep video color propagation"

Connected through hyperedge HE-2 (induced by source paper "natural image matting via guided contextual attention"):
  with members:
    - "inductive guided filter real time deep image matting with weakly annotated masks on mobile devices"
    - "indices matter learning to index for deep image matting"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "alphagan generative adversarial networks for natural image matting"
    - "bag of tricks for image classification with convolutional neural networks"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generative image inpainting with contextual attention"

Connected through hyperedge HE-3 (induced by source paper "scopeflow dynamic scene scoping for optical flow"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "deep video super resolution using hr optical flow estimation"):
  with members:
    - "frame recurrent video super resolution"
    - "scale recurrent network for deep image deblurring"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "learning for video super resolution through hr optical flow estimation"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "pyramid stereo matching network"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-5 (induced by source paper "self supervised object in gripper segmentation from robotic motions"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "towards segmenting everything that moves"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "photorealistic image synthesis for object instance detection"
    - "deep neural network concepts for background subtraction a systematic review and comparative evaluation"
    - "learning instance segmentation by interaction"
    - "deep visual domain adaptation a survey"
    - "object discovery in videos as foreground motion clustering"

Connected through hyperedge HE-6 (induced by source paper "occlusion aware unsupervised learning of optical flow from video"):
  with members:
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "unsupervised deep epipolar flow for stationary or dynamic scenes"
    - "learning optical flow via dilated networks and occlusion reasoning"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "digging into self supervised monocular depth estimation"
    - "hidden two stream convolutional networks for action recognition"

Connected through hyperedge HE-7 (induced by source paper "stc flow spatio temporal context aware optical flow estimation"):
  with members:
    - "iterative residual refinement for joint optical flow and occlusion estimation"
    - "selflow self supervised learning of optical flow"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "hierarchical discrete distribution decomposition for match density estimation"
    - "cbam convolutional block attention module"
    - "dual attention network for scene segmentation"
    - "non local neural networks"
    - "relation networks for object detection"

Connected through hyperedge HE-8 (induced by source paper "sense a shared encoder network for scene flow estimation"):
  with members:
    - "deep rigid instance scene flow"
    - "learning rigidity in dynamic scenes with a moving camera for 3d motion field estimation"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "segstereo exploiting semantic information for disparity estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "unified perceptual parsing for scene understanding"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "combining stereo disparity and optical flow for basic scene flow"

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)