human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "optical flow requires multiple strategies but only one network"
  Abstract: We show that the matching problem that underlies optical flow requires multiple strategies, depending on the amount of image motion and other factors. We then study the implications of this observation on training a deep neural network for representing image patches in the context of descriptor based optical flow. We propose a metric learning method, which selects suitable negative samples based on the nature of the true match. This type of training produces a network that displays multiple strategies depending on the input and leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow benchmarks.

Connected through hyperedge HE-1 (induced by source paper "flowfields accurate optical flow correspondences meet robust interpolation"):
  with members:
    - "optical flow in mostly rigid scenes"
    - "mirrorflow exploiting symmetries in joint optical flow and occlusion estimation"
    - "accurate optical flow via direct cost volume processing"
    - "probflow joint optical flow and uncertainty estimation"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "sceneflowfields dense interpolation of sparse scene flow correspondences"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "exploiting semantic information and deep matching for optical flow"

Connected through hyperedge HE-2 (induced by source paper "transflow unsupervised motion flow by joint geometric and pixel level estimation"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "cnn based patch matching for optical flow with thresholded hinge embedding loss"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "spatial transformer networks"
    - "flownet learning optical flow with convolutional networks"
    - "epicflow edge preserving interpolation of correspondences for optical flow"

Connected through hyperedge HE-3 (induced by source paper "fast predictive simple geodesic regression"):
  with members:
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "quicksilver fast predictive image registration a deep learning approach"
    - "video frame synthesis using deep voxel flow"
    - "fast predictive image registration"
    - "adam a method for stochastic optimization"

Question: Please tell me which class the center node 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)