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 "the five elements of flow"):
  Abstract: In this work we propose five concrete steps to improve the performance of optical flow algorithms. We carefully reviewed recently introduced innovations and well-established techniques in deep learning-based flow methods including i) pyramidal feature representations, ii) flow-based consistency checks, iii) cost volume construction practices or iv) distillation, and present extensions or alternatives to inhibiting factors we identified therein. We also show how changing the way gradients propagate in modern flow networks can lead to surprising boosts in performance. Finally, we contribute a novel feature that adaptively guides the learning process towards improving on under-performing flow predictions. Our findings are conceptually simple and easy to implement, yet result in compelling improvements on relevant error measures that we demonstrate via exhaustive ablations on datasets like Flying Chairs2, Flying Things, Sintel and KITTI. We establish new state-of-the-art results on the challenging Sintel and Kitti 2015 test datasets, and even show the portability of our findings to different optical flow and depth from stereo approaches.

Includes members:
  Member "a lightweight optical flow cnn revisiting data fidelity and regularization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scopeflow dynamic scene scoping for optical flow")
  Member "sense a shared encoder network for scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning end to end scene flow by distilling single tasks knowledge")
  Member "iterative residual refinement for joint optical flow and occlusion estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "foal fast online adaptive learning for cardiac motion estimation")
  Member "ddflow learning optical flow with unlabeled data distillation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization")
  Member "selflow self supervised learning of optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization")
  Member "models matter so does training an empirical study of cnns for optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "scopeflow dynamic scene scoping for optical flow")
  Member "a fusion approach for multi frame optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "belief propagation reloaded learning bp layers for labeling problems")

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)