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 "inverting deep generative models one layer at a time"):
  Abstract: We study the problem of inverting a deep generative model with ReLU activations. Inversion corresponds to finding a latent code vector that explains observed measurements as much as possible. In most prior works this is performed by attempting to solve a non-convex optimization problem involving the generator. In this paper we obtain several novel theoretical results for the inversion problem. #R##N#We show that for the realizable case, single layer inversion can be performed exactly in polynomial time, by solving a linear program. Further, we show that for multiple layers, inversion is NP-hard and the pre-image set can be non-convex. #R##N#For generative models of arbitrary depth, we show that exact recovery is possible in polynomial time with high probability, if the layers are expanding and the weights are randomly selected. Very recent work analyzed the same problem for gradient descent inversion. Their analysis requires significantly higher expansion (logarithmic in the latent dimension) while our proposed algorithm can provably reconstruct even with constant factor expansion. We also provide provable error bounds for different norms for reconstructing noisy observations. Our empirical validation demonstrates that we obtain better reconstructions when the latent dimension is large.

Includes members:
  Member "asymptotics of map inference in deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "rate optimal denoising with deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "denoising and regularization via exploiting the structural bias of convolutional generators")
  Member "deep decoder concise image representations from untrained non convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "image recognition from raw labels collected without annotators")
  Member "random mesh projectors for inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "one dimensional deep image prior for time series inverse problems")
  Member "neural proximal gradient descent for compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "sunlayer stable denoising with generative networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "phase retrieval under a generative prior":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")
  Member "deep denoising rate optimal recovery of structured signals with a deep prior":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast and provable admm for learning with generative priors")

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)