human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "models matter so does training an empirical study of cnns for optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scopeflow dynamic scene scoping for optical flow")
  Member "flowfields accurate optical flow correspondences meet robust interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation")
  Member "a fusion approach for multi frame optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scopeflow dynamic scene scoping for optical flow")
  Member "proflow learning to predict optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scopeflow dynamic scene scoping for optical flow")
  Member "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")
  Member "liteflownet a lightweight convolutional neural network for optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "softmax splatting for video frame interpolation")
  Member "segmentation aware convolutional networks using local attention masks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "segmentation aware image denoising without knowing true segmentation")
  Member "optical flow in mostly rigid scenes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization")

Question: Please tell me which class the source paper 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)