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: "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"
  Abstract: Optical flow estimation is one of the most studied problems in computer vision, yet recent benchmark datasets continue to reveal problem areas of today's approaches. Occlusions have remained one of the key challenges. In this paper, we propose a symmetric optical flow method to address the well-known chicken-and-egg relation between optical flow and occlusions. In contrast to many state-of-the-art methods that consider occlusions as outliers, possibly filtered out during post-processing, we highlight the importance of joint occlusion reasoning in the optimization and show how to utilize occlusion as an important cue for estimating optical flow. The key feature of our model is to fully exploit the symmetry properties that characterize optical flow and occlusions in the two consecutive images. Specifically through utilizing forward-backward consistency and occlusion-disocclusion symmetry in the energy, our model jointly estimates optical flow in both forward and backward direction, as well as consistent occlusion maps in both views. We demonstrate significant performance benefits on standard benchmarks, especially from the occlusion-disocclusion symmetry. On the challenging KITTI dataset we report the most accurate two-frame results to date.

Connected through hyperedge HE-1 (induced by source paper "deltra deep light transport for projector camera systems"):
  with members:
    - "pro cam ssfm projector camera system for structure and spectral reflectance from motion"
    - "compennet end to end full projector compensation"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "self calibrating deep photometric stereo networks"
    - "end to end projector photometric compensation"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "a single shot per pose camera projector calibration system for imperfect planar targets"
    - "inverserendernet learning single image inverse rendering"

Connected through hyperedge HE-2 (induced by source paper "multigrid predictive filter flow for unsupervised learning on videos"):
  with members:
    - "learning correspondence from the cycle consistency of time"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised geometry aware representation for 3d human pose estimation"
    - "deep clustering for unsupervised learning of visual features"
    - "efficient video object segmentation via network modulation"
    - "object discovery in videos as foreground motion clustering"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-3 (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"

Connected through hyperedge HE-4 (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"
    - "flowfields accurate optical flow correspondences meet robust interpolation"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "mono sf multi view geometry meets single view depth for monocular scene flow estimation of dynamic traffic scenes"):
  with members:
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "deep rigid instance scene flow"
    - "lightweight probabilistic deep networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-6 (induced by source paper "recurrent neural network for un supervised learning of monocular videovisual odometry and depth"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "single view stereo matching"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "geometric consistency for self supervised end to end visual odometry"
    - "megadepth learning single view depth prediction from internet photos"

Connected through hyperedge HE-7 (induced by source paper "pwoc 3d deep occlusion aware end to end scene flow estimation"):
  with members:
    - "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "fast multi frame stereo scene flow with motion segmentation"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "cascaded scene flow prediction using semantic segmentation"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "optical flow estimation using a spatial pyramid network"

Connected through hyperedge HE-8 (induced by source paper "df net unsupervised joint learning of depth and flow using cross task consistency"):
  with members:
    - "learning single image depth from videos using quality assessment networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deepmvs learning multi view stereopsis"
    - "taskonomy disentangling task transfer learning"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "megadepth learning single view depth prediction from internet photos"

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)