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: "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
  Abstract: Real-world data processing problems often involve various image modalities associated with a certain scene, including RGB images, infrared images or multi-spectral images. The fact that different image modalities often share certain attributes, such as certain edges, textures and other structure primitives, represents an opportunity to enhance various image processing tasks. This paper proposes a new approach to construct a high-resolution (HR) version of a low-resolution (LR) image given another HR image modality as reference, based on joint sparse representations induced by coupled dictionaries. Our approach, which captures the similarities and disparities between different image modalities in a learned sparse feature domain in \emph{lieu} of the original image domain, consists of two phases. The coupled dictionary learning phase is used to learn a set of dictionaries that couple different image modalities in the sparse feature domain given a set of training data. In turn, the coupled super-resolution phase leverages such coupled dictionaries to construct a HR version of the LR target image given another related image modality. One of the merits of our sparsity-driven approach relates to the fact that it overcomes drawbacks such as the texture copying artifacts commonly resulting from inconsistency between the guidance and target images. Experiments on real multimodal images demonstrate that incorporating appropriate guidance information via joint sparse representation induced by coupled dictionary learning brings notable benefits in the super-resolution task with respect to the state-of-the-art. Of particular relevance, the proposed approach also demonstrates better robustness than competing deep-learning-based methods in the presence of noise.

Connected through hyperedge HE-1 (induced by source paper "pyramidal edge maps based guided thermal super resolution"):
  with members:
    - "keyframe based direct thermal inertial odometry"
    - "multimodal sensor fusion in single thermal image super resolution"
    - "multimedia super resolution via deep learning a survey"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "richer convolutional features for edge detection"
    - "fully convolutional siamese networks for object tracking"
    - "densely connected convolutional networks"
    - "a deep primal dual network for guided depth super resolution"

Connected through hyperedge HE-2 (induced by source paper "deep convolutional neural network for multi modal image restoration and fusion"):
  with members:
    - "densefuse a fusion approach to infrared and visible images"
    - "feedback network for image super resolution"
    - "deformable kernel networks for joint image filtering"
    - "fast end to end trainable guided filter"
    - "co occurrence filter"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-3 (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"
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "multi modal dictionary learning for image separation with application in art investigation"
    - "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-4 (induced by source paper "multimodal image super resolution via deep unfolding with side information"):
  with members:
    - "deep back projection networks for super resolution"
    - "image super resolution via dual state recurrent networks"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "enhanced deep residual networks for single image super resolution"
    - "maximal sparsity with deep networks"
    - "accurate image super resolution using very deep convolutional networks"
    - "compressed sensing with side information geometrical interpretation and performance bounds"

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)