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: "fastmri an open dataset and benchmarks for accelerated mri"
  Abstract: Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive. We introduce the fastMRI dataset, a large-scale collection of both raw MR measurements and clinical MR images, that can be used for training and evaluation of machine-learning approaches to MR image reconstruction. By introducing standardized evaluation criteria and a freely-accessible dataset, our goal is to help the community make rapid advances in the state of the art for MR image reconstruction. We also provide a self-contained introduction to MRI for machine learning researchers with no medical imaging background.

Connected through hyperedge HE-1 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction"):
  with members:
    - "understanding geometry of encoder decoder cnns"
    - "boosting cnn beyond label in inverse problems"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "k space deep learning for reference free epi ghost correction"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "understanding batch normalization"
    - "image reconstruction by domain transform manifold learning"
    - "deep generative adversarial networks for compressed sensing automates mri"

Connected through hyperedge HE-2 (induced by source paper "nnoculation broad spectrum and targeted treatment of backdoored dnns"):
  with members:
    - "are adversarial perturbations a showstopper for ml based cad a case study on cnn based lithographic hotspot detection"
    - "strip a defence against trojan attacks on deep neural networks"
    - "defending neural backdoors via generative distribution modeling"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "generative adversarial networks an overview"
    - "spectral signatures in backdoor attacks"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "plug and play methods for magnetic resonance imaging long version"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "solving inverse computational imaging problems using deep pixel level prior"
    - "image reconstruction by domain transform manifold learning"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-4 (induced by source paper "evalai towards better evaluation systems for ai agents"):
  with members:
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "parlai a dialog research software platform"
    - "embodied question answering"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "ai2 thor an interactive 3d environment for visual ai"
    - "mask r cnn"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"

Connected through hyperedge HE-5 (induced by source paper "block coordinate regularization by denoising"):
  with members:
    - "deepred deep image prior powered by red"
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "deep mean shift priors for image restoration"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"

Connected through hyperedge HE-6 (induced by source paper "scaling laws for the principled design initialization and preconditioning of relu networks"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "signsgd compressed optimisation for non convex problems"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "diagonal rescaling for neural networks"
    - "neural architecture search with reinforcement learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "reducing uncertainty in undersampled mri reconstruction with active acquisition"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "leveraging uncertainty estimates for predicting segmentation quality"
    - "k space deep learning for accelerated mri"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "image reconstruction by domain transform manifold learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "energy based generative adversarial network"

Connected through hyperedge HE-8 (induced by source paper "optimal transport cyclegan and penalized ls for unsupervised learning in inverse problems"):
  with members:
    - "modl model based deep learning architecture for inverse problems"
    - "cycle consistent adversarial denoising network for multiphase coronary ct angiography"
    - "deep compressed sensing"
    - "k space deep learning for accelerated mri"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "learning deep cnn denoiser prior for image restoration"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"

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)