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: "on instabilities of deep learning in image reconstruction does ai come at a cost"
  Abstract: Deep learning, due to its unprecedented success in tasks such as image classification, has emerged as a new tool in image reconstruction with potential to change the field. In this paper we demonstrate a crucial phenomenon: deep learning typically yields unstablemethods for image reconstruction. The instabilities usually occur in several forms: (1) tiny, almost undetectable perturbations, both in the image and sampling domain, may result in severe artefacts in the reconstruction, (2) a small structural change, for example a tumour, may not be captured in the reconstructed image and (3) (a counterintuitive type of instability) more samples may yield poorer performance. Our new stability test with algorithms and easy to use software detects the instability phenomena. The test is aimed at researchers to test their networks for instabilities and for government agencies, such as the Food and Drug Administration (FDA), to secure safe use of deep learning methods.

Connected through hyperedge HE-1 (induced by source paper "the gap between theory and practice in function approximation with deep neural networks"):
  with members:
    - "deep network approximation for smooth functions"
    - "the troublesome kernel why deep learning for inverse problems is typically unstable"
    - "greedy shallow networks a new approach for constructing and training neural networks"
    - "deep double descent where bigger models and more data hurt"
    - "growing axons greedy learning of neural networks with application to function approximation"
    - "a polynomial based approach for architectural design and learning with deep neural networks"
    - "neural network integral representations with the relu activation function"
    - "energy and policy considerations for deep learning in nlp"

Connected through hyperedge HE-2 (induced by source paper "improving robustness of deep learning based image reconstruction"):
  with members:
    - "a direct approach to robust deep learning using adversarial networks"
    - "gan based projector for faster recovery with convergence guarantees in linear inverse problems"
    - "evaluating robustness of deep image super resolution against adversarial attacks"
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "scaling provable adversarial defenses"
    - "adversarially robust generalization requires more data"
    - "generating adversarial examples with adversarial networks"
    - "image reconstruction by domain transform manifold learning"

Connected through hyperedge HE-3 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable"):
  with members:
    - "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling"
    - "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis"
    - "learning the invisible a hybrid deep learning shearlet framework for limited angle computed tomography"
    - "adversarial attacks and defences a survey"
    - "image reconstruction by domain transform manifold learning"
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-4 (induced by source paper "deep neural networks with trainable activations and controlled lipschitz constant"):
  with members:
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "exploring generalization in deep learning"
    - "deep convolutional neural network for inverse problems in imaging"
    - "understanding deep neural networks with rectified linear units"
    - "u net convolutional networks for biomedical image segmentation"
    - "deepfool a simple and accurate method to fool deep neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning activation functions to improve deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "pyro nn python reconstruction operators in neural networks"):
  with members:
    - "a gentle introduction to deep learning in medical image processing"
    - "image reconstruction by domain transform manifold learning"
    - "precision learning towards use of known operators in neural networks"
    - "deep convolutional neural network for inverse problems in imaging"
    - "mr to x ray projection image synthesis"
    - "wavenet a generative model for raw audio"
    - "u net convolutional networks for biomedical image segmentation"

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)