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: "adversarial regularizers in inverse problems"
  Abstract: Inverse Problems in medical imaging and computer vision are traditionally solved using purely model-based methods. Among those variational regularization models are one of the most popular approaches. We propose a new framework for applying data-driven approaches to inverse problems, using a neural network as a regularization functional. The network learns to discriminate between the distribution of ground truth images and the distribution of unregularized reconstructions. Once trained, the network is applied to the inverse problem by solving the corresponding variational problem. Unlike other data-based approaches for inverse problems, the algorithm can be applied even if only unsupervised training data is available. Experiments demonstrate the potential of the framework for denoising on the BSDS dataset and for computer tomography reconstruction on the LIDC dataset.

Connected through hyperedge HE-1 (induced by source paper "the oracle of dlphi"):
  with members:
    - "nfft meets krylov methods fast matrix vector products for the graph laplacian of fully connected networks"
    - "a haar wavelet based perceptual similarity index for image quality assessment"
    - "the universal approximation power of finite width deep relu networks"
    - "mathematics of deep learning"
    - "underwater object classification using scattering transform of sonar signals"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "error bounds for approximations with deep relu networks"
    - "benefits of depth in neural networks"

Connected through hyperedge HE-2 (induced by source paper "deepflow history matching in the space of deep generative models"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "cgans with projection discriminator"
    - "analyzing inverse problems with invertible neural networks"
    - "probabilistic semantic inpainting with pixel constrained cnns"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "sampling generative networks"
    - "improved training of wasserstein gans"
    - "reconstruction of three dimensional porous media using generative adversarial neural networks"

Connected through hyperedge HE-3 (induced by source paper "bilevel integrative optimization for ill posed inverse problems"):
  with members:
    - "learning converged propagations with deep prior ensemble for image enhancement"
    - "end to end learning of motion representation for video understanding"
    - "fast and accurate single image super resolution via information distillation network"
    - "on the convergence of learning based iterative methods for nonconvex inverse problems"
    - "non local recurrent network for image restoration"
    - "a theoretically guaranteed deep optimization framework for robust compressive sensing mri"
    - "deep back projection networks for super resolution"
    - "non local neural networks"

Connected through hyperedge HE-4 (induced by source paper "a projectional ansatz to reconstruction"):
  with members:
    - "deepred deep image prior powered by red"
    - "regularization by architecture a deep prior approach for inverse problems"
    - "deep image prior"
    - "plug and play priors for bright field electron tomography and sparse interpolation"
    - "the little engine that could regularization by denoising red"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "random mesh projectors for inverse problems"):
  with members:
    - "image reconstruction by domain transform manifold learning"
    - "deep image prior"
    - "deep learning for photoacoustic tomography from sparse data"
    - "deep convolutional neural network for inverse problems in imaging"
    - "deep learning guided image reconstruction from incomplete data"
    - "maximal sparsity with deep networks"
    - "identity mappings in deep residual networks"
    - "amortised map inference for image 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)