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 "beyond low rank a data adaptive tensor completion method"):
  Abstract: Low rank tensor representation underpins much of recent progress in tensor completion. In real applications, however, this approach is confronted with two challenging problems, namely (1) tensor rank determination; (2) handling real tensor data which only approximately fulfils the low-rank requirement. To address these two issues, we develop a data-adaptive tensor completion model which explicitly represents both the low-rank and non-low-rank structures in a latent tensor. Representing the non-low-rank structure separately from the low-rank one allows priors which capture the important distinctions between the two, thus enabling more accurate modelling, and ultimately, completion. Through defining a new tensor rank, we develop a sparsity induced prior for the low-rank structure, with which the tensor rank can be automatically determined. The prior for the non-low-rank structure is established based on a mixture of Gaussians which is shown to be flexible enough, and powerful enough, to inform the completion process for a variety of real tensor data. With these two priors, we develop a Bayesian minimum mean squared error estimate (MMSE) framework for inference which provides the posterior mean of missing entries as well as their uncertainty. Compared with the state-of-the-art methods in various applications, the proposed model produces more accurate completion results.

Includes members:
  Member "bayesian robust tensor factorization for incomplete multiway data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian tensorized neural networks with automatic rank selection")
  Member "parallel matrix factorization for low rank tensor completion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "low rank tensor grid for image 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 "encoding high dimensional local features by sparse coding based fisher vectors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep cnns meet global covariance pooling better representation and generalization")
  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 "infinite tucker decomposition nonparametric bayesian models for multiway data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning compact recurrent neural networks with block term tensor decomposition")

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)