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 "algorithms and software for projections onto intersections of convex and non convex sets with applications to inverse problems"):
  Abstract: We propose algorithms and software for computing projections onto the intersection of multiple convex and non-convex constraint sets. The software package, called SetIntersectionProjection, is intended for the regularization of inverse problems in physical parameter estimation and image processing. The primary design criterion is working with multiple sets, which allows us to solve inverse problems with multiple pieces of prior knowledge. Our algorithms outperform the well known Dykstra's algorithm when individual sets are not easy to project onto because we exploit similarities between constraint sets. Other design choices that make the software fast and practical to use, include recently developed automatic selection methods for auxiliary algorithm parameters, fine and coarse grained parallelism, and a multilevel acceleration scheme. We provide implementation details and examples that show how the software can be used to regularize inverse problems. Results show that we benefit from working with all available prior information and are not limited to one or two regularizers because of algorithmic, computational, or hyper-parameter selection issues.

Includes members:
  Member "a unified 2d 3d large scale software environment for nonlinear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an event driven approach to serverless seismic imaging in the cloud")
  Member "adaptive relaxed admm convergence theory and practical implementation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a comprehensive survey for low rank regularization")
  Member "an inner loop free solution to inverse problems using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast compressive sensing recovery using generative models with structured latent variables")
  Member "image restoration using autoencoding priors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning generative models using denoising density estimators")
  Member "adaptive consensus admm for distributed optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "doublesqueeze parallel stochastic gradient descent with double pass error compensated compression")
  Member "devito automated fast finite difference computation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high level python abstractions for optimal checkpointing in inversion problems")
  Member "adaptive admm with spectral penalty parameter selection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "catching cheats detecting strategic manipulation in distributed optimisation of electric vehicle aggregators")
  Member "plug and play admm for image restoration fixed point convergence and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blind image restoration without prior knowledge")

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.MS(Mathematical Software)