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 "accurate optical flow via direct cost volume processing"):
  Abstract: We present an optical flow estimation approach that operates on the full four-dimensional cost volume. This direct approach shares the structural benefits of leading stereo matching pipelines, which are known to yield high accuracy. To this day, such approaches have been considered impractical due to the size of the cost volume. We show that the full four-dimensional cost volume can be constructed in a fraction of a second due to its regularity. We then exploit this regularity further by adapting semi-global matching to the four-dimensional setting. This yields a pipeline that achieves significantly higher accuracy than state-of-the-art optical flow methods while being faster than most. Our approach outperforms all published general-purpose optical flow methods on both Sintel and KITTI 2015 benchmarks.

Includes members:
  Member "exploiting semantic information and deep matching for optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantically guided representation learning for self supervised monocular depth")
  Member "optical flow with semantic segmentation and localized layers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sense a shared encoder network for scene flow estimation")
  Member "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "full flow optical flow estimation by global optimization over regular grids":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sceneflowfields multi frame matching visibility prediction and robust interpolation for scene flow estimation")
  Member "joint optical flow and temporally consistent semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sense a shared encoder network for scene flow estimation")
  Member "flow fields dense correspondence fields for highly accurate large displacement optical flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning depth via interaction")
  Member "deep compression compressing deep neural networks with pruning trained quantization and huffman coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "stereo matching by training a convolutional neural network to compare image patches":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mini unmanned aerial vehicle based remote sensing techniques applications and prospects")

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)