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: "continuous stereo matching using local expansion moves"
  Abstract: We present an accurate and efficient stereo matching method using local expansion moves, a new move making scheme using graph cuts. The local expansion moves are presented as many alpha-expansions defined for small grid regions. The local expansion moves extend the traditional expansion moves by two ways: localization and spatial propagation. By localization, we use different candidate alpha-labels according to the locations of local alpha-expansions. By spatial propagation, we design our local alpha-expansions to propagate currently assigned labels for nearby regions. With this localization and spatial propagation, our method can efficiently infer Markov random field models with a huge or continuous label space using a randomized search scheme. Our local expansion move method has several advantages over previous approaches that are based on fusion moves or belief propagation; it produces submodular moves deriving a subproblem optimality; it helps find good, smooth, piecewise linear disparity maps; it is suitable for parallelization; it can use cost-volume filtering techniques for accelerating the matching cost computations. Our method is evaluated using the Middlebury stereo benchmark and shown to have the best performance in sub-pixel accuracy.

Connected through hyperedge HE-1 (induced by source paper "left right comparative recurrent model for stereo matching"):
  with members:
    - "continuous 3d label stereo matching using local expansion moves"
    - "self supervised learning for stereo matching with self improving ability"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "end to end learning of geometry and context for deep stereo regression"
    - "semi supervised deep learning for monocular depth map prediction"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "detect replace refine deep structured prediction for pixel wise labeling"

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)