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 the matching function"
  Abstract: The matching function for the problem of stereo reconstruction or optical flow has been traditionally designed as a function of the distance between the features describing matched pixels. This approach works under assumption, that the appearance of pixels in two stereo cameras or in two consecutive video frames does not change dramatically. However, this might not be the case, if we try to match pixels over a large interval of time. #R##N#In this paper we propose a method, which learns the matching function, that automatically finds the space of allowed changes in visual appearance, such as due to the motion blur, chromatic distortions, different colour calibration or seasonal changes. Furthermore, it automatically learns the importance of matching scores of contextual features at different relative locations and scales. Proposed classifier gives reliable estimations of pixel disparities already without any form of regularization. #R##N#We evaluated our method on two standard problems - stereo matching on KITTI outdoor dataset, optical flow on Sintel data set, and on newly introduced TimeLapse change detection dataset. Our algorithm obtained very promising results comparable to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "unsupervised monocular stereo matching"):
  with members:
    - "single view stereo matching"
    - "unsupervised learning of depth and ego motion from video"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "semi supervised deep learning for monocular depth map prediction"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"

Connected through hyperedge HE-2 (induced by source paper "single view stereo matching"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "semi supervised deep learning for monocular depth map prediction"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "deeper depth prediction with fully convolutional residual networks"

Connected through hyperedge HE-3 (induced by source paper "look wider to match image patches with convolutional neural networks"):
  with members:
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "flownet learning optical flow with convolutional networks"
    - "learning deconvolution network for semantic segmentation"
    - "learning to compare image patches via convolutional neural networks"
    - "very deep convolutional networks for large scale image recognition"
    - "object detectors emerge in deep scene cnns"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-4 (induced by source paper "unsupervised monocular depth estimation with left right consistency"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deeper depth prediction with fully convolutional residual networks"
    - "estimating depth from monocular images as classification using deep fully convolutional residual networks"

Connected through hyperedge HE-5 (induced by source paper "dense semantic correspondence where every pixel is a classifier"):
  with members:
    - "high speed tracking with kernelized correlation filters"
    - "efficient object localization using convolutional networks"
    - "learning detectors quickly using structured covariance matrices"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

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)