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: "iterative residual refinement for joint optical flow and occlusion estimation"
  Abstract: Deep learning approaches to optical flow estimation have seen rapid progress over the recent years. One common trait of many networks is that they refine an initial flow estimate either through multiple stages or across the levels of a coarse-to-fine representation. While leading to more accurate results, the downside of this is an increased number of parameters. Taking inspiration from both classical energy minimization approaches as well as residual networks, we propose an iterative residual refinement (IRR) scheme based on weight sharing that can be combined with several backbone networks. It reduces the number of parameters, improves the accuracy, or even achieves both. Moreover, we show that integrating occlusion prediction and bi-directional flow estimation into our IRR scheme can further boost the accuracy. Our full network achieves state-of-the-art results for both optical flow and occlusion estimation across several standard datasets.

Connected through hyperedge HE-1 (induced by source paper "foal fast online adaptive learning for cardiac motion estimation"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "learning a probabilistic model for diffeomorphic registration"
    - "deep compressed sensing"
    - "direct object recognition without line of sight using optical coherence"
    - "selflow self supervised learning of optical flow"
    - "learning to self train for semi supervised few shot classification"
    - "unsupervised event based learning of optical flow depth and egomotion"
    - "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"

Connected through hyperedge HE-2 (induced by source paper "scopeflow dynamic scene scoping for optical flow"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "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"
    - "proflow learning to predict optical flow"

Connected through hyperedge HE-3 (induced by source paper "stc flow spatio temporal context aware optical flow estimation"):
  with members:
    - "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"
    - "liteflownet a lightweight convolutional neural network for optical flow 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-4 (induced by source paper "resdepth learned residual stereo reconstruction"):
  with members:
    - "dsm building shape refinement from combined remote sensing images based on wnet cgans"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "deepmvs learning multi view stereopsis"
    - "pyramid stereo matching network"
    - "semantically informed multiview surface refinement"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "learning for disparity estimation through feature constancy"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"

Connected through hyperedge HE-5 (induced by source paper "the five elements of flow"):
  with members:
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "sense a shared encoder network for scene flow estimation"
    - "ddflow learning optical flow with unlabeled data distillation"
    - "selflow self supervised learning of optical flow"
    - "models matter so does training an empirical study of cnns for optical flow estimation"
    - "a fusion approach for multi frame optical flow estimation"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "hierarchical discrete distribution decomposition for match density 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)