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: "pyramidal gradient matching for optical flow estimation"
  Abstract: Initializing optical flow field by either sparse descriptor matching or dense patch matches has been proved to be particularly useful for capturing large displacements. In this paper, we present a pyramidal gradient matching approach that can provide dense matches for highly accurate and efficient optical flow estimation. A novel contribution of our method is that image gradient is used to describe image patches and proved to be able to produce robust matching. Therefore, our method is more efficient than methods that adopt special features (like SIFT) or patch distance metric. Moreover, we find that image gradient is scalable for optical flow estimation, which means we can use different levels of gradient feature (for example, full gradients or only direction information of gradients) to obtain different complexity without dramatic changes in accuracy. Another contribution is that we uncover the secrets of limited PatchMatch through a thorough analysis and design a pyramidal matching framework based these secrets. Our pyramidal matching framework is aimed at robust gradient matching and effective to grow inliers and reject outliers. In this framework, we present some special enhancements for outlier filtering in gradient matching. By initializing EpicFlow with our matches, experimental results show that our method is efficient and robust (ranking 1st on both clean pass and final pass of MPI Sintel dataset among published methods).

Connected through hyperedge HE-1 (induced by source paper "unsupervised learning of geometry with edge aware depth normal consistency"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "semi supervised deep learning for monocular depth map prediction"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deeper depth prediction with fully convolutional residual networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "spatial transformer networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)