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 "global guarantees for enforcing deep generative priors by empirical risk"):
  Abstract: We examine the theoretical properties of enforcing priors provided by generative deep neural networks via empirical risk minimization. In particular we consider two models, one in which the task is to invert a generative neural network given access to its last layer and another in which the task is to invert a generative neural network given only compressive linear observations of its last layer. We establish that in both cases, in suitable regimes of network layer sizes and a randomness assumption on the network weights, that the non-convex objective function given by empirical risk minimization does not have any spurious stationary points. That is, we establish that with high probability, at any point away from small neighborhoods around two scalar multiples of the desired solution, there is a descent direction. Hence, there are no local minima, saddle points, or other stationary points outside these neighborhoods. These results constitute the first theoretical guarantees which establish the favorable global geometry of these non-convex optimization problems, and they bridge the gap between the empirical success of enforcing deep generative priors and a rigorous understanding of non-linear inverse problems.

Includes members:
  Member "how generative adversarial networks and their variants work an overview":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "towards moderate overparameterization global convergence guarantees for training shallow neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "medgan medical image translation using gans")
  Member "learning overparameterized neural networks via stochastic gradient descent on structured data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "phase retrieval under a generative prior":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")
  Member "segan adversarial network with multi scale l 1 loss for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions")
  Member "a convergence theory for deep learning via over parameterization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "unsupervised reverse domain adaptation for synthetic medical images via adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

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.IT(Information Theory)