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: "joint optical flow and temporally consistent semantic segmentation"
  Abstract: The importance and demands of visual scene understanding have been steadily increasing along with the active development of autonomous systems. Consequently, there has been a large amount of research dedicated to semantic segmentation and dense motion estimation. In this paper, we propose a method for jointly estimating optical flow and temporally consistent semantic segmentation, which closely connects these two problem domains and leverages each other. Semantic segmentation provides information on plausible physical motion to its associated pixels, and accurate pixel-level temporal correspondences enhance the accuracy of semantic segmentation in the temporal domain. We demonstrate the benefits of our approach on the KITTI benchmark, where we observe performance gains for flow and segmentation. We achieve state-of-the-art optical flow results, and outperform all published algorithms by a large margin on challenging, but crucial dynamic objects.

Connected through hyperedge HE-1 (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-2 (induced by source paper "learning multi object tracking and segmentation from automatic annotations"):
  with members:
    - "video instance segmentation"
    - "disentangling monocular 3d object detection"
    - "seamless scene segmentation"
    - "mots multi object tracking and segmentation"
    - "panoptic deeplab"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "combined image and world space tracking in traffic scenes"

Connected through hyperedge HE-3 (induced by source paper "every frame counts joint learning of video segmentation and optical flow"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "selflow self supervised learning of optical flow"
    - "deep spatio temporal random fields for efficient video segmentation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "low latency video semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "improving semantic segmentation via video propagation and label relaxation"

Connected through hyperedge HE-4 (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-5 (induced by source paper "layered optical flow estimation using a deep neural network with a soft mask"):
  with members:
    - "optical flow estimation using a spatial pyramid network"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "unsupervised convolutional neural networks for motion estimation"
    - "deepstereo learning to predict new views from the world s imagery"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "fast piecewise affine motion estimation without segmentation"):
  with members:
    - "optical flow with semantic segmentation and localized layers"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "aggregation of local parametric candidates with exemplar based occlusion handling for optical flow"
    - "sparsity based methods for overparameterized variational problems"

Connected through hyperedge HE-7 (induced by source paper "semantic video cnns through representation warping"):
  with members:
    - "understanding convolution for semantic segmentation"
    - "fast optical flow using dense inverse search"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "video propagation networks"
    - "clockwork convnets for video semantic segmentation"
    - "temporal segment networks towards good practices for deep action recognition"
    - "playing for data ground truth from computer games"

Connected through hyperedge HE-8 (induced by source paper "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"):
  with members:
    - "optical flow in mostly rigid scenes"
    - "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 with semantic segmentation and localized layers"
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"
    - "full flow optical flow estimation by global optimization over regular grids"
    - "flow fields dense correspondence fields for highly accurate large displacement optical 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)