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: "x ray image separation via coupled dictionary learning"
  Abstract: In support of art investigation, we propose a new source separation method that unmixes a single X-ray scan acquired from double-sided paintings. Unlike prior source separation methods, which are based on statistical or structural incoherence of the sources, we use visual images taken from the front- and back-side of the panel to drive the separation process. The coupling of the two imaging modalities is achieved via a new multi-scale dictionary learning method. Experimental results demonstrate that our method succeeds in the discrimination of the sources, while state-of-the-art methods fail to do so.

Connected through hyperedge HE-1 (induced by source paper "multi modal image processing based on coupled dictionary learning"):
  with members:
    - "multi modal dictionary learning for image separation with application in art investigation"
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "hyperspectral pansharpening a review"
    - "collaborative multi sensor classification via sparsity based representation"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-2 (induced by source paper "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"):
  with members:
    - "multi modal dictionary learning for image separation with application in art investigation"
    - "accelerating the super resolution convolutional neural network"
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "accurate image super resolution using very deep convolutional networks"
    - "hyperspectral pansharpening a review"
    - "deeply recursive convolutional network for image super resolution"
    - "collaborative multi sensor classification via sparsity based representation"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "multi modal dictionary learning for image separation with application in art investigation"):
  with members:
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "compressed sensing with side information geometrical interpretation and performance bounds"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "bayesian crack detection in ultra high resolution multimodal images of paintings"
    - "exact recovery of sparsely used dictionaries"
    - "modified cs modifying compressive sensing for problems with partially known support"

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)