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 "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation"):
  Abstract: State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness. With the successful concept of pixel-wise matching and sparse-to-dense interpolation, we shift the operating point in this field of conflicts towards universality and speed. Avoiding strong assumptions on the domain or the problem yields a more robust algorithm. This algorithm is fast because we avoid explicit regularization during matching, which allows an efficient computation. Using image information from multiple time steps and explicit visibility prediction based on previous results, we achieve competitive performances on different data sets. Our contributions and results are evaluated in comparative experiments. Overall, we present an accurate scene flow algorithm that is faster and more generic than any individual benchmark leader.

Includes members:
  Member "pwoc 3d deep occlusion aware end to end scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning end to end scene flow by distilling single tasks knowledge")
  Member "deep rigid instance scene flow":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "budgeted training rethinking deep neural network training under resource constraints")
  Member "flowfields accurate optical flow correspondences meet robust interpolation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "iterative residual refinement for joint optical flow and occlusion estimation")
  Member "deep visual domain adaptation a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "combining stereo disparity and optical flow for basic scene flow":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sense a shared encoder network for scene flow estimation")
  Member "fast multi frame stereo scene flow with motion segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning end to end scene flow by distilling single tasks knowledge")
  Member "probflow joint optical flow and uncertainty estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning representations for predicting future activities")
  Member "cascaded scene flow prediction using semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sense a shared encoder network for scene flow estimation")

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)