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: "flowfields accurate optical flow correspondences meet robust interpolation"
  Abstract: Optical Flow algorithms are of high importance for many applications. Recently, the Flow Field algorithm and its modifications have shown remarkable results, as they have been evaluated with top accuracy on different data sets. In our analysis of the algorithm we have found that it produces accurate sparse matches, but there is room for improvement in the interpolation. Thus, we propose in this paper FlowFields++, where we combine the accurate matches of Flow Fields with a robust interpolation. In addition, we propose improved variational optimization as post-processing. Our new algorithm is evaluated on the challenging KITTI and MPI Sintel data sets with public top results on both 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"
    - "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"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"

Connected through hyperedge HE-2 (induced by source paper "iterative residual refinement for joint optical flow and occlusion estimation"):
  with members:
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a fusion approach for multi frame optical flow estimation"
    - "proflow learning to predict optical flow"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "segmentation aware convolutional networks using local attention masks"
    - "optical flow in mostly rigid scenes"
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"

Connected through hyperedge HE-3 (induced by source paper "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"):
  with members:
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "understanding convolution for semantic segmentation"
    - "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"

Connected through hyperedge HE-4 (induced by source paper "delio decoupled lidar odometry"):
  with members:
    - "imls slam scan to model matching based on 3d data"
    - "limo lidar monocular visual odometry"
    - "combining stereo disparity and optical flow for basic scene flow"
    - "segmatch segment based place recognition in 3d point clouds"
    - "cnn for imu assisted odometry estimation using velodyne lidar"
    - "a review of mean shift algorithms for clustering"
    - "approximate k nearest neighbour based spatial clustering using k d tree"

Connected through hyperedge HE-5 (induced by source paper "a compact light field camera for real time depth estimation"):
  with members:
    - "fast and efficient depth map estimation from light fields"
    - "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"

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)