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: "regularization by architecture a deep prior approach for inverse problems"
  Abstract: The present paper studies the so called deep image prior (DIP) technique in the context of inverse problems. DIP networks have been introduced recently for applications in image processing, also first experimental results for applying DIP to inverse problems have been reported. This paper aims at discussing different interpretations of DIP and to obtain analytic results for specific network designs and linear operators. The main contribution is to introduce the idea of viewing these approaches as the optimization of Tiknonov functionals rather than optimizing networks. Besides theoretical results, we present numerical verifications for an academic example (integration operator) as well as for the inverse problem of magnetic particle imaging (MPI). The reconstructions obtained by deep prior networks are compared with state of the art methods.

Connected through hyperedge HE-1 (induced by source paper "translating diffusion wavelets and regularisation into residual networks"):
  with members:
    - "pde based group equivariant convolutional neural networks"
    - "analysis of invariance and robustness via invertibility of relu networks"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"
    - "networks for nonlinear diffusion problems in imaging"
    - "wavelet convolutional neural networks"
    - "forward stability of resnet and its variants"
    - "deep image prior"

Connected through hyperedge HE-2 (induced by source paper "one dimensional deep image prior for time series inverse problems"):
  with members:
    - "brits bidirectional recurrent imputation for time series"
    - "deep decoder concise image representations from untrained non convolutional networks"
    - "random mesh projectors for inverse problems"
    - "phase retrieval under a generative prior"
    - "image restoration using total variation regularized deep image prior"
    - "deep image prior"
    - "recurrent neural networks for multivariate time series with missing values"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "a projectional ansatz to reconstruction"):
  with members:
    - "deepred deep image prior powered by red"
    - "adversarial regularizers in 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-4 (induced by source paper "neural reparameterization improves structural optimization"):
  with members:
    - "global optimization of dielectric metasurfaces using a physics driven neural network"
    - "3d topology optimization using convolutional neural networks"
    - "neural networks for topology optimization"
    - "deep image prior"
    - "multi content gan for few shot font style transfer"
    - "automated derivation of the adjoint of high level transient finite element programs"

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.LG(Machine Learning)