human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable"):
  Abstract: There is overwhelming empirical evidence that Deep Learning (DL) leads to unstable methods in applications ranging from image classification and computer vision to voice recognition and automated diagnosis in medicine. Recently, a similar instability phenomenon has been discovered when DL is used to solve certain problems in computational science, namely, inverse problems in imaging. In this paper we present a comprehensive mathematical analysis explaining the many facets of the instability phenomenon in DL for inverse problems. Our main results not only explain why this phenomenon occurs, they also shed light as to why finding a cure for instabilities is so difficult in practice. Additionally, these theorems show that instabilities are typically not rare events - rather, they can occur even when the measurements are subject to completely random noise - and consequently how easy it can be to destablise certain trained neural networks. We also examine the delicate balance between reconstruction performance and stability, and in particular, how DL methods may outperform state-of-the-art sparse regularization methods, but at the cost of instability. Finally, we demonstrate a counterintuitive phenomenon: training a neural network may generically not yield an optimal reconstruction method for an inverse problem.

Includes members:
  Member "uniform recovery in infinite dimensional compressed sensing and applications to structured binary sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")
  Member "on instabilities of deep learning in image reconstruction does ai come at a cost":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the gap between theory and practice in function approximation with deep neural networks")
  Member "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non uniform recovery guarantees for binary measurements and infinite dimensional compressed sensing")
  Member "learning the invisible a hybrid deep learning shearlet framework for limited angle computed tomography":
    also connected through other hyperedges:
      (none)
  Member "adversarial attacks and defences a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ai safety state of the field through quantitative lens")
  Member "image reconstruction by domain transform manifold learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "cnn based projected gradient descent for consistent ct image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep neural networks with trainable activations and controlled lipschitz constant")
  Member "optimal approximation of continuous functions by very deep relu networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural contextual bandits with ucb based exploration")

Question: Please tell me which class the source paper 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)