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 "bilinear parameterization for differentiable rank regularization"):
  Abstract: Low rank approximation is a commonly occurring problem in many computer vision and machine learning applications. There are two common ways of optimizing the resulting models. Either the set of matrices with a given sought rank can be explicitly parametrized using a bilinear factorization, or low rank can be implicitly enforced using regularization terms penalizing non-zero singular values. While the former results in differentiable problems that can be efficiently optimized using local quadratic approximation the latter are typically not differentiable (sometimes even discontinuous) and require splitting methods such as Alternating Direction Method of Multipliers (ADMM). It is well known that while ADMM makes rapid improvements the first couple of iterations convergence to the exact minimizer can be tediously slow. On the other hand regularization formulations can in certain cases come with theoretical optimality guarantees. #R##N#In this paper we show how many non-differentiable regularization methods can be reformulated into smooth objectives using bilinear parameterization. This opens up the possibility of using second order methods such as Levenberg--Marquardt (LM) and Variable Projection (VarPro) to achieve accurate solutions for ill-conditioned problems. We show on several real and synthetic experiments that our second order formulation converges to substantially more accurate solutions than what ADMM formulations provide in a reasonable amount of time.

Includes members:
  Member "bilinear factor matrix norm minimization for robust pca algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "structured low rank matrix factorization global optimality algorithms and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "clustering with similarity preserving")
  Member "no spurious local minima in nonconvex low rank problems a unified geometric analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "no spurious local minima in deep quadratic networks")
  Member "matrix completion has no spurious local minimum":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "partial sum minimization of singular values in robust pca algorithm and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "generalized nonconvex nonsmooth low rank minimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "convex relaxations of structured matrix factorizations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "binary component decomposition part i the positive semidefinite case")

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)