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 "scopeflow dynamic scene scoping for optical flow"):
  Abstract: We propose to modify the common training protocols of optical flow, leading to sizable accuracy improvements without adding to the computational complexity of the training process. The improvement is based on observing the bias in sampling challenging data that exists in the current training protocol, and improving the sampling process. In addition, we find that both regularization and augmentation should decrease during the training protocol. #R##N#Using a low parameters off-the-shelf model, the method is ranked first on the MPI Sintel benchmark among all other methods, improving the best two frames method accuracy by more than 10%. The method also surpasses all similar architecture variants by more than 12% and 19.7% on the KITTI benchmarks, achieving the lowest Average End-Point Error on KITTI2012 among two-frame methods, without using extra datasets.

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 "glu net global local universal network for dense flow and correspondences")
  Member "iterative residual refinement for joint optical flow and occlusion estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (induced by source paper "joint unsupervised learning of optical flow and egomotion with bi level optimization")
  Member "what makes good synthetic training data for learning disparity and optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "how much real data do we actually need analyzing object detection performance using synthetic and real data")
  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 "sense a shared encoder network for scene flow estimation")
  Member "a fusion approach for multi frame optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "iterative residual refinement for joint optical flow and occlusion estimation")
  Member "dgc net dense geometric correspondence network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "geometric image correspondence verification by dense pixel matching")

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)