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 "missing slice recovery for tensors using a low rank model in embedded space"):
  Abstract: Let us consider a case where all of the elements in some continuous slices are missing in tensor data. #R##N#In this case, the nuclear-norm and total variation regularization methods usually fail to recover the missing elements. #R##N#The key problem is capturing some delay/shift-invariant structure. #R##N#In this study, we consider a low-rank model in an embedded space of a tensor. #R##N#For this purpose, we extend a delay embedding for a time series to a "multi-way delay-embedding transform" for a tensor, which takes a given incomplete tensor as the input and outputs a higher-order incomplete Hankel tensor. #R##N#The higher-order tensor is then recovered by Tucker-based low-rank tensor factorization. #R##N#Finally, an estimated tensor can be obtained by using the inverse multi-way delay embedding transform of the recovered higher-order tensor. #R##N#Our experiments showed that the proposed method successfully recovered missing slices for some color images and functional magnetic resonance images.

Includes members:
  Member "low rank tensor completion a riemannian manifold preconditioning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")
  Member "smooth parafac decomposition for tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "provable model for tensor ring completion")
  Member "bayesian cp factorization of incomplete tensors with automatic rank determination":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "low rank tensor grid for image completion")
  Member "the power of convex relaxation near optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "matrix completion with noise":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "exact matrix completion via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)