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 "investigating task driven latent feasibility for nonconvex image modeling"):
  Abstract: Properly modeling the latent image distributions always plays a key role in a variety of low-level vision problems. Most existing approaches, such as Maximum A Posterior (MAP), aimed at establishing optimization models with prior regularization to address this task. However, designing sophisticated priors may lead to challenging optimization model and time-consuming iteration process. Recent studies tried to embed learnable network architectures into the MAP scheme. Unfortunately, for the MAP model with deeply trained priors, the exact behaviors and the inference process are actually hard to investigate, due to their inexact and uncontrolled nature. In this work, by investigating task-driven latent feasibility for the MAP-based model, we provide a new perspective to enforce domain knowledge and data distributions to MAP-based image modeling. Specifically, we first introduce an energy-based feasibility constraint to the given MAP model. By introducing the proximal gradient updating scheme to the objective and performing an adaptive averaging process, we obtain a completely new MAP inference process, named Proximal Average Optimization (PAO), for image modeling. Owning to the flexibility of PAO, we can also incorporate deeply trained architectures into the feasibility module. Finally, we provide a simple monotone descent-based control mechanism to guide the propagation of PAO. We prove in theory that the sequence generated by both our PAO and its learning-based extension can successfully converge to the critical point of the original MAP optimization task. We demonstrate how to apply our framework to address different vision applications. Extensive experiments verify the theoretical results and show the advantages of our method against existing state-of-the-art approaches.

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 "plug and play methods provably converge with properly trained denoisers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep plug and play prior for low rank tensor completion")
  Member "deep plug and play super resolution for arbitrary blur kernels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic frequency masking to improve super resolution and denoising networks")
  Member "regularization by denoising clarifications and new interpretations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "neural blind deconvolution using deep priors")
  Member "acceleration of red via vector extrapolation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "regularization by denoising clarifications and new interpretations")
  Member "convergence analysis of proximal gradient with momentum for nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization")
  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)