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 "tensor q rank a new data dependent tensor rank"):
  Abstract: Recently, the \textit{Tensor Nuclear Norm~(TNN)} regularization based on t-SVD has been widely used in various low tubal-rank tensor recovery tasks. However, these models usually require smooth change of data along the third dimension to ensure their low rank structures. In this paper, we propose a new definition of tensor rank named \textit{tensor Q-rank} by a column orthonormal matrix $\mathbf{Q}$, and further make $\mathbf{Q}$ data-dependent. #R##N#We introduce an explainable selection method of $\mathbf{Q}$, under which the data tensor may have a more significant low tensor Q-rank structure than that of low tubal-rank structure. #R##N#We also provide a corresponding envelope of our rank function and apply it to the low rank tensor completion problem. Then we give an effective algorithm and briefly analyze why our method works better than TNN based methods in the case of complex data with low sampling rate. Finally, experimental results on real-world datasets demonstrate the superiority of our proposed model in the tensor completion problem.

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 "low rank tensor completion a riemannian manifold preconditioning approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "a unified alternating direction method of multipliers by majorization minimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  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 "nuclear norm of higher order tensors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multiresolution tensor learning for efficient and interpretable spatial analysis")
  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 "a new convex relaxation for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the convergence of admm with task adaption and beyond")

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)