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: "an unsupervised approach to solving inverse problems using generative adversarial networks"
  Abstract: Solving inverse problems continues to be a challenge in a wide array of applications ranging from deblurring, image inpainting, source separation etc. Most existing techniques solve such inverse problems by either explicitly or implicitly finding the inverse of the model. The former class of techniques require explicit knowledge of the measurement process which can be unrealistic, and rely on strong analytical regularizers to constrain the solution space, which often do not generalize well. The latter approaches have had remarkable success in part due to deep learning, but require a large collection of source-observation pairs, which can be prohibitively expensive. In this paper, we propose an unsupervised technique to solve inverse problems with generative adversarial networks (GANs). Using a pre-trained GAN in the space of source signals, we show that one can reliably recover solutions to under determined problems in a `blind' fashion, i.e., without knowledge of the measurement process. We solve this by making successive estimates on the model and the solution in an iterative fashion. We show promising results in three challenging applications -- blind source separation, image deblurring, and recovering an image from its edge map, and perform better than several baselines.

Connected through hyperedge HE-1 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing"):
  with members:
    - "improved hyperspectral unmixing with endmember variability parametrized using an interpolated scaling tensor"
    - "a low rank tensor regularization strategy for hyperspectral unmixing"
    - "a data dependent multiscale model for hyperspectral unmixing with spectral variability"
    - "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"
    - "low rank tensor modeling for hyperspectral unmixing accounting for spectral variability"
    - "improved deep spectral convolution network for hyperspectral unmixing with multinomial mixture kernel and endmember uncertainty"
    - "band selection for nonlinear unmixing of hyperspectral images as a maximal clique problem"
    - "generalized linear mixing model accounting for endmember variability"

Connected through hyperedge HE-2 (induced by source paper "training image estimators without image ground truth"):
  with members:
    - "learning intrinsic image decomposition from watching the world"
    - "noise2noise learning image restoration without clean data"
    - "training deep learning based image denoisers from undersampled measurements without ground truth and without image prior"
    - "deep semantic face deblurring"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "unsupervised learning of depth and ego motion from video"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"

Connected through hyperedge HE-3 (induced by source paper "gan based projector for faster recovery in compressed sensing with convergence guarantees"):
  with members:
    - "a gpu oriented algorithm design for secant based dimensionality reduction"
    - "an inner loop free solution to inverse problems using deep neural networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

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)