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 "local kernels that approximate bayesian regularization and proximal operators"):
  Abstract: In this paper, we broadly connect kernel-based filtering (e.g., approaches such as the bilateral filter and non-local means, but also many more) with general variational formulations of Bayesian regularized least squares and the related concept of proximal operators. Variational/Bayesian/proximal formulations often result in optimization problems that do not have closed-form solutions and therefore typically require  global  iterative solutions. Our main contribution here is to establish how one can approximate the solution of the resulting global optimization problems using locally adaptive filters with specific kernels. Our results are valid for small regularization strength (i.e., weak noise), but the approach is powerful enough to be useful for a wide range of applications because we expose how to derive a “kernelized” solution to these problems that approximates the global solution in one shot, using only local operations. As another side benefit in the reverse direction, given a local data-adaptive filter constructed with a particular choice of kernel, we enable the interpretation of such filters in the variational/Bayesian/proximal framework.

Includes members:
  Member "a general and adaptive robust loss function":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "derivative manipulation for general example weighting")
  Member "learning proximal operators using denoising networks for regularizing inverse imaging problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  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")

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)