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: "learning optical flow via dilated networks and occlusion reasoning"
  Abstract: Despite the significant progress that has been made on estimating optical flow recently, most estimation methods, including classical and deep learning approaches, still have difficulty with multi-scale estimation, real-time computation, and/or occlusion reasoning. In this paper, we introduce dilated convolution and occlusion reasoning into unsupervised optical flow estimation to address these issues. The dilated convolution allows our network to avoid upsampling via deconvolution and the resulting gridding artifacts. Dilated convolution also results in a smaller memory footprint which speeds up interference. The occlusion reasoning prevents our network from learning incorrect deformations due to occluded image regions during training. Our proposed method outperforms state-of-the-art unsupervised approaches on the KITTI benchmark. We also demonstrate its generalization capability by applying it to action recognition in video.

Connected through hyperedge HE-1 (induced by source paper "occlusion aware unsupervised learning of optical flow from video"):
  with members:
    - "self supervised learning with geometric constraints in monocular video connecting flow depth and camera"
    - "unsupervised deep epipolar flow for stationary or dynamic scenes"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "every pixel counts joint learning of geometry and motion with 3d holistic understanding"
    - "liteflownet a lightweight convolutional neural network for optical flow estimation"
    - "digging into self supervised monocular depth estimation"
    - "hidden two stream convolutional networks for action recognition"

Connected through hyperedge HE-2 (induced by source paper "random temporal skipping for multirate video analysis"):
  with members:
    - "deep texture manifold for ground terrain recognition"
    - "towards universal representation for unseen action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep local video feature for action recognition"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "hidden two stream convolutional networks for action recognition"
    - "densenet for dense flow"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-3 (induced by source paper "hidden two stream convolutional networks for action recognition"):
  with members:
    - "video enhancement with task oriented flow"
    - "deep texture manifold for ground terrain recognition"
    - "towards universal representation for unseen action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "deep local video feature for action recognition"
    - "compressed video action recognition"

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)