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 "invertible generative models for inverse problems mitigating representation error and dataset bias"):
  Abstract: Trained generative models have shown remarkable performance as priors for inverse problems in imaging. For example, Generative Adversarial Network priors permit recovery of test images from 5-10x fewer measurements than sparsity priors. Unfortunately, these models may be unable to represent any particular image because of architectural choices, mode collapse, and bias in the training dataset. In this paper, we demonstrate that invertible neural networks, which have zero representation error by design, can be effective natural signal priors at inverse problems such as denoising, compressive sensing, and inpainting. Our formulation is an empirical risk minimization that does not directly optimize the likelihood of images, as one would expect. Instead we optimize the likelihood of the latent representation of images as a proxy, as this is empirically easier. For compressive sensing, our formulation can yield higher accuracy than sparsity priors across almost all undersampling ratios. For the same accuracy on test images, they can use 10-20x fewer measurements. We demonstrate that invertible priors can yield better reconstructions than sparsity priors for images that have rare features of variation within the biased training set, including out-of-distribution natural images.

Includes members:
  Member "medical image synthesis for data augmentation and anonymization using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hi net hybrid fusion network for multi modal mr image synthesis")
  Member "analyzing inverse problems with invertible neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  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 "finger gan generating realistic fingerprint images using connectivity imposed gan":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "level three synthetic fingerprint generation")
  Member "latent convolutional models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "image processing using multi code gan prior")
  Member "blind image deconvolution using deep generative priors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep s 3 pr simultaneous source separation and phase retrieval using deep generative models")
  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 "i revnet deep invertible networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "explaining knowledge distillation by quantifying the knowledge")

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.CV(Computer Vision and Pattern Recognition)