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 "plug and play unplugged optimization free reconstruction using consensus equilibrium"):
  Abstract: Regularized inversion methods for image reconstruction are used widely due to their tractability and their ability to combine complex physical sensor models with useful regularity criteria. Such methods were used in the recently developed Plug-and-Play prior method, which provides a framework to use advanced denoising algorithms as regularizers in inversion. However, the need to formulate regularized inversion as the solution to an optimization problem severely limits both the expressiveness of possible regularity conditions and the variety of provably convergent Plug-and-Play denoising operators. #R##N#In this paper, we introduce the concept of consensus equilibrium (CE), which generalizes regularized inversion to include a much wider variety of regularity operators without the need for an optimization formulation. Consensus equilibrium is based on the solution of a set of equilibrium equations that balance data fit and regularity. In this framework, the problem of MAP estimation in regularized inversion is replaced by the problem of solving these equilibrium equations, which can be approached in multiple ways, including as a fixed point problem that generalizes the ADMM approach used in the Plug-and-Play method. #R##N#We present the Douglas-Rachford (DR) algorithm for computing the CE solution as a fixed point and prove the convergence of this algorithm under conditions that include denoising operators that do not arise from optimization problems and that may not be nonexpansive. We give several examples to illustrate the idea of consensus equilibrium and the convergence properties of the DR algorithm and demonstrate this method on a sparse interpolation problem using electron microscopy data.

Includes members:
  Member "optimal combination of image denoisers":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for image denoising a survey")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "the little engine that could regularization by denoising red":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ada lista learned solvers adaptive to varying models")
  Member "multi resolution data fusion for super resolution electron microscopy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "plug and play admm for image restoration fixed point convergence and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blind image restoration without prior knowledge")
  Member "poisson inverse problems by the plug and play scheme":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plug and play methods provably converge with properly trained denoisers")

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)