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: "interponet a brain inspired neural network for optical flow dense interpolation"
  Abstract: Sparse-to-dense interpolation for optical flow is a fundamental phase in the pipeline of most of the leading optical flow estimation algorithms. The current state-of-the-art method for interpolation, EpicFlow, is a local average method based on an edge aware geodesic distance. We propose a new data-driven sparse-to-dense interpolation algorithm based on a fully convolutional network. We draw inspiration from the filling-in process in the visual cortex and introduce lateral dependencies between neurons and multi-layer supervision into our learning process. We also show the importance of the image contour to the learning process. Our method is robust and outperforms EpicFlow on competitive optical flow benchmarks with several underlying matching algorithms. This leads to state-of-the-art performance on the Sintel and KITTI 2012 benchmarks.

Connected through hyperedge HE-1 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation"):
  with members:
    - "pwoc 3d deep occlusion aware end to end scene flow estimation"
    - "deep rigid instance scene flow"
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "deep visual domain adaptation a survey"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "probflow joint optical flow and uncertainty estimation"
    - "cascaded scene flow prediction using semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "a lightweight optical flow cnn revisiting data fidelity and regularization"):
  with members:
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "accurate optical flow via direct cost volume processing"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"

Connected through hyperedge HE-3 (induced by source paper "ddflow learning optical flow with unlabeled data distillation"):
  with members:
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "accurate optical flow via direct cost volume processing"
    - "data distillation towards omni supervised learning"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"

Connected through hyperedge HE-4 (induced by source paper "hallucinating dense optical flow from sparse lidar for autonomous vehicles"):
  with members:
    - "deep lidar cnn to understand the dynamics of moving vehicles"
    - "joint coarse and fine reasoning for deep optical flow"
    - "deconvolutional networks for point cloud vehicle detection and tracking in driving scenarios"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "squeezeseg convolutional neural nets with recurrent crf for real time road object segmentation from 3d lidar point cloud"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "exploiting semantic information and deep matching for optical flow"

Connected through hyperedge HE-5 (induced by source paper "flowfields accurate optical flow correspondences meet robust interpolation"):
  with members:
    - "optical flow in mostly rigid scenes"
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"
    - "accurate optical flow via direct cost volume processing"
    - "probflow joint optical flow and uncertainty estimation"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "exploiting semantic information and deep matching for optical flow"

Connected through hyperedge HE-6 (induced by source paper "learning energy based inpainting for optical flow"):
  with members:
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "scalable full flow with learned binary descriptors"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "training deep nets with sublinear memory cost"
    - "theano a python framework for fast computation of mathematical expressions"
    - "atgv net accurate depth super resolution"

Connected through hyperedge HE-7 (induced by source paper "sparsity invariant cnns"):
  with members:
    - "oriented response networks"
    - "demon depth and motion network for learning monocular stereo"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "harmonic networks deep translation and rotation equivariance"
    - "learning structured sparsity in deep neural networks"
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "vehicle detection from 3d lidar using fully convolutional network"

Connected through hyperedge HE-8 (induced by source paper "pwc net cnns for optical flow using pyramid warping and cost volume"):
  with members:
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "optical flow in mostly rigid scenes"
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"
    - "accurate optical flow via direct cost volume processing"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "exploiting semantic information and deep matching for optical flow"
    - "optical flow estimation using a spatial pyramid network"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

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)