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 "a novel nonconvex approach to recover the low tubal rank tensor data when t svd meets pssv"):
  Abstract: A recently developed novel tensor decomposition scheme named tensor singular value decomposition (t-SVD) results in a notion of rank referred to as the tubal-rank. Many methods minimize its convex surrogate the tensor nuclear norm (TNN) to enhance the low tubal-rankness of the underlying data. Generally, minimizing the TNN may cause some biases. In this paper, to alleviate these bias phenomenons, we consider to minimize the proposed partial sum of the tensor nuclear norm (PSTNN) in place of the TNN. The novel PSTNN is applied to the tasks of tensor completion and tensor principal component analysis. Numerical experiments are conducted on the synthetic data and real-world data, and experimental results reveal the effectiveness of the proposed methods.

Includes members:
  Member "weighted low rank tensor recovery for hyperspectral image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d quasi recurrent neural network for hyperspectral image denoising")
  Member "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm")
  Member "partial sum minimization of singular values in robust pca algorithm and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "parallel matrix factorization for low rank tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low rank tensor grid for image completion")
  Member "exact tensor completion using t svd":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "low rank tensor grid for image completion")
  Member "generalized singular value thresholding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a benchmark for sparse coding when group sparsity meets rank minimization")
  Member "an alternating direction algorithm for matrix completion with nonnegative factors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a group norm regularized lrr factorization model for spectral clustering")

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.NA(Numerical Analysis)