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 sparse representations of depth"
  Abstract: This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, such as that obtained by laser range scanners or structured light depth cameras. Sparse representations are learned from the Middlebury database disparity maps and then exploited in a two-layer graphical model for inferring depth from stereo, by including a sparsity prior on the learned features. Since they capture higher order dependencies in the depth structure, these priors can complement smoothness priors commonly used in depth inference based on Markov random field (MRF) models. Inference on the proposed graph is achieved using an alternating iterative optimization technique, where the first layer is solved using an existing MRF-based stereo matching algorithm, then held fixed as the second layer is solved using the proposed nonstationary sparse coding algorithm. This leads to a general method for improving solutions of state-of-the-art MRF-based depth estimation algorithms. Our experimental results first show that depth inference using learned representations leads to state-of-the-art denoising of depth maps obtained from laser range scanners and a time of flight camera. Furthermore, we show that adding sparse priors improves the results of depth estimation methods based on graph cut optimization of MRFs with first and second order priors.

Connected through hyperedge HE-1 (induced by source paper "efficient dictionary learning with sparseness enforcing projections"):
  with members:
    - "sparse activity and sparse connectivity in supervised learning"
    - "block coordinate descent for sparse nmf"
    - "separable dictionary learning"
    - "feature grouping from spatially constrained multiplicative interaction"
    - "estimating unknown sparsity in compressed sensing"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "comparing measures of sparsity"
    - "non negative matrix factorization with sparseness constraints"

Connected through hyperedge HE-2 (induced by source paper "sparse modeling for image and vision processing"):
  with members:
    - "sparsity based poisson denoising with dictionary learning"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "generalized max pooling"
    - "fast and robust archetypal analysis for representation learning"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "invariant scattering convolution networks"

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)