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: "multi modal dictionary learning for image separation with application in art investigation"
  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. In this problem, the X-ray signals to be separated have similar morphological characteristics, which brings previous source separation methods to their limits. Our solution is to use photographs taken from the front-and back-side of the panel to drive the separation process. The crux of our approach relies on the coupling of the two imaging modalities (photographs and X-rays) using a novel coupled dictionary learning framework able to capture both common and disparate features across the modalities using parsimonious representations; the common component captures features shared by the multi-modal images, whereas the innovation component captures modality-specific information. As such, our model enables the formulation of appropriately regularized convex optimization procedures that lead to the accurate separation of the X-rays. Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement. Moreover, to improve further on the visual quality of the separated images, we propose to train coupled dictionaries that ignore certain parts of the painting corresponding to craquelure. Experimentation on synthetic and real data—taken from digital acquisition of the Ghent Altarpiece (1432)—confirms the superiority of our method against the state-of-the-art morphological component analysis technique that uses either fixed or trained dictionaries to perform image separation.

Connected through hyperedge HE-1 (induced by source paper "deep coupled representation learning for sparse linear inverse problems with side information"):
  with members:
    - "an approximate message passing framework for side information"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "from denoising to compressed sensing"
    - "deep unfolding model based inspiration of novel deep architectures"

Connected through hyperedge HE-2 (induced by source paper "multi modal image processing based on coupled dictionary learning"):
  with members:
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "x ray image separation via coupled dictionary learning"
    - "hyperspectral pansharpening a review"
    - "collaborative multi sensor classification via sparsity based representation"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-3 (induced by source paper "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"):
  with members:
    - "accelerating the super resolution convolutional neural network"
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "x ray image separation via coupled dictionary learning"
    - "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"

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)