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 "on the convergence of admm with task adaption and beyond"):
  Abstract: Along with the development of learning and vision, Alternating Direction Method of Multiplier (ADMM) has become a popular algorithm for separable optimization model with linear constraint. However, the ADMM and its numerical variants (e.g., inexact, proximal or linearized) are awkward to obtain state-of-the-art performance when dealing with complex learning and vision tasks due to their weak task-adaption ability. Recently, there has been an increasing interest in incorporating task-specific computational modules (e.g., designed filters or learned architectures) into ADMM iterations. Unfortunately, these task-related modules introduce uncontrolled and unstable iterative flows, they also break the structures of the original optimization model. Therefore, existing theoretical investigations are invalid for these resulted task-specific iterations. In this paper, we develop a simple and generic proximal ADMM framework to incorporate flexible task-specific module for learning and vision problems. We rigorously prove the convergence both in objective function values and the constraint violation and provide the worst-case convergence rate measured by the iteration complexity. Our investigations not only develop new perspectives for analyzing task-adaptive ADMM but also supply meaningful guidelines on designing practical optimization methods for real-world applications. Numerical experiments are conducted to verify the theoretical results and demonstrate the efficiency of our algorithmic framework.

Includes members:
  Member "fastderain a novel video rain streak removal method using directional gradient priors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm")
  Member "regularization by denoising clarifications and new interpretations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "on the convergence of learning based iterative methods for nonconvex inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural blind deconvolution using deep priors")
  Member "an online plug and play algorithm for regularized image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "density aware single image de raining using a multi stream dense network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cooperative semantic segmentation and image restoration in adverse environmental conditions")
  Member "a theoretically guaranteed deep optimization framework for robust compressive sensing mri":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bilevel integrative optimization for ill posed inverse problems")
  Member "toward designing convergent deep operator splitting methods for task specific nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning collaborative generation correction modules for blind image deblurring and beyond")
  Member "clearing the skies a deep network architecture for single image rain removal":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments")

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)