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: "recurrent inference machines for solving inverse problems"
  Abstract: Inverse problems are typically solved by first defining a model and then choosing an inference procedure. With this separation of modeling from inference, inverse problems can be framed in a modular way. For example, variational inference can be applied to a broad class of models. The modularity, however, typically goes away after model parameters have been trained under a chosen inference procedure. During training, model and inference often interact in a way that the model parameters will ultimately be adapted to the chosen inference procedure, posing the two components inseparable after training. But if model and inference become inseperable after training, why separate them in the first place?#N##N#We propose a novel learning framework which abandons the dichotomy between model and inference. Instead, we introduce Recurrent Inference Machines (RIM), a class of recurrent neural networks (RNN) that directly learn to solve inverse problems.#N##N#We demonstrate the effectiveness of RIMs in experiments on various image reconstruction tasks. We show empirically that RIMs exhibit the desirable convergence behavior of classical inference procedures, and that they can outperform state-of- the-art methods when trained on specialized inference tasks.#N##N#Our approach bridges the gap between inverse problems and deep learning, providing a framework for fast progression in the field of inverse problems.

Connected through hyperedge HE-1 (induced by source paper "neural enhanced belief propagation on factor graphs"):
  with members:
    - "factor graph neural network"
    - "iterative amortized inference"
    - "inference in probabilistic graphical models by graph neural networks"
    - "self normalizing neural networks"
    - "the consciousness prior"
    - "neural message passing for quantum chemistry"
    - "learning to learn by gradient descent by gradient descent"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-2 (induced by source paper "the mertens unrolled network mu net a high dynamic range fusion neural network for through the windshield driver recognition"):
  with members:
    - "high dynamic range image forensics using cnn"
    - "retinaface single stage dense face localisation in the wild"
    - "arcface additive angular margin loss for deep face recognition"
    - "expandnet a deep convolutional neural network for high dynamic range expansion from low dynamic range content"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "separating reflection and transmission images in the wild"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-3 (induced by source paper "modl model based deep learning architecture for inverse problems"):
  with members:
    - "model based free breathing cardiac mri reconstruction using deep learned storm priors modl storm"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"
    - "unrolled optimization with deep priors"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"

Connected through hyperedge HE-4 (induced by source paper "invert to learn to invert"):
  with members:
    - "analyzing inverse problems with invertible neural networks"
    - "invertible residual networks"
    - "i revnet deep invertible networks"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "the reversible residual network backpropagation without storing activations"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "density estimation using real nvp"

Connected through hyperedge HE-5 (induced by source paper "mr image reconstruction using deep density priors"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "image reconstruction by domain transform manifold learning"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "tackling over pruning in variational autoencoders"
    - "deep convolutional neural network for inverse problems in imaging"
    - "bayesian image quality transfer with cnns exploring uncertainty in dmri super resolution"
    - "a survey on deep learning in medical image analysis"
    - "recurrent generative adversarial networks for proximal learning and automated compressive image recovery"

Connected through hyperedge HE-6 (induced by source paper "degrees of freedom analysis of unrolled neural networks"):
  with members:
    - "training deep learning based denoisers without ground truth data"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unrolled optimization with deep priors"
    - "a deep cascade of convolutional neural networks for mr image reconstruction"
    - "the little engine that could regularization by denoising red"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "raisr rapid and accurate image super resolution"
    - "super resolution with deep convolutional sufficient statistics"

Connected through hyperedge HE-7 (induced by source paper "optimization algorithm inspired deep neural network structure design"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing"
    - "practical block wise neural network architecture generation"
    - "convolutional neural fabrics"
    - "maximal sparsity with deep networks"
    - "deep convolutional neural networks with merge and run mappings"
    - "densely connected convolutional networks"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "deep learning for passive synthetic aperture radar"):
  with members:
    - "deep convolutional neural network for inverse problems in imaging"
    - "learning to learn by gradient descent by gradient descent"
    - "onsager corrected deep learning for sparse linear inverse problems"
    - "a deep learning approach to block based compressed sensing of images"
    - "a deep learning approach to structured signal recovery"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "adam a method for stochastic optimization"

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.NE(Neural and Evolutionary Computing)