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: "deep image super resolution via natural image priors"
  Abstract: Single image super-resolution (SR) via deep learning has recently gained significant attention in the literature. Convolutional neural networks (CNNs) are typically learned to represent the mapping between low-resolution (LR) and high-resolution (HR) images/patches with the help of training examples. Most existing deep networks for SR produce high quality results when training data is abundant. However, their performance degrades sharply when training is limited. We propose to regularize deep structures with prior knowledge about the images so that they can capture more structural information from the same limited data. In particular, we incorporate in a tractable fashion within the CNN framework, natural image priors which have shown to have much recent success in imaging and vision inverse problems. Experimental results show that the proposed deep network with natural image priors is particularly effective in training starved regimes.

Connected through hyperedge HE-1 (induced by source paper "adaptive transform domain image super resolution via orthogonally regularized deep networks"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "orthogonally regularized deep networks for image super resolution"
    - "residual dense network for image super resolution"
    - "a frequency domain neural network for fast image super resolution"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "sparsity based color image super resolution via exploiting cross channel constraints"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-2 (induced by source paper "cooperative neural networks conn exploiting prior independence structure for improved classification"):
  with members:
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "dynamic routing between capsules"
    - "a deep and autoregressive approach for topic modeling of multimodal data"
    - "discriminative embeddings of latent variable models for structured data"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "neural variational inference for text processing"
    - "spatial transformer networks"
    - "end to end learning of lda by mirror descent back propagation over a deep architecture"

Connected through hyperedge HE-3 (induced by source paper "learning from a handful volumes mri resolution enhancement with volumetric super resolution forests"):
  with members:
    - "brain mri super resolution using 3d deep densely connected neural networks"
    - "benchmarking super resolution algorithms on real data"
    - "enhanced deep residual networks for single image super resolution"
    - "bayesian image quality transfer with cnns exploring uncertainty in dmri super resolution"

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)