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 "exact recovery of tensor robust principal component analysis under linear transforms"):
  Abstract: This work studies the Tensor Robust Principal Component Analysis (TRPCA) problem, which aims to exactly recover the low-rank and sparse components from their sum. Our model is motivated by the recently proposed linear transforms based tensor-tensor product and tensor SVD. We define a new transforms depended tensor rank and the corresponding tensor nuclear norm. Then we solve the TRPCA problem by convex optimization whose objective is a weighted combination of the new tensor nuclear norm and the $\ell_1$-norm. In theory, we show that under certain incoherence conditions, the convex program exactly recovers the underlying low-rank and sparse components. It is of great interest that our new TRPCA model generalizes existing works. In particular, if the studied tensor reduces to a matrix, our TRPCA model reduces to the known matrix RPCA. Our new TRPCA which is allowed to use general linear transforms can be regarded as an extension of our former TRPCA work which uses the discrete Fourier transform. But their proof of the recovery guarantee is different. Numerical experiments verify our results and the application on image recovery demonstrates the superiority of our method.

Includes members:
  Member "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization":
    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 "a unified alternating direction method of multipliers by majorization minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "incoherence optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "exact tensor completion using t svd":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low rank tensor grid for image completion")
  Member "novel methods for multilinear data completion and de noising based on tensor svd":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "low rank tensor grid for image completion")
  Member "a new convex relaxation for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "the power and arnoldi methods in an algebra of circulants":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalized visual information analysis via tensorial algebra")
  Member "most tensor problems are np hard":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "provable tensor ring completion")

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.LG(Machine Learning)