human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "recovery of future data via convolution nuclear norm minimization"
  Abstract: This paper is about recovering the unseen future data from a given sequence of historical samples, so called as future data recovery---a significant problem closely related to time series forecasting. For the first time, we study the problem from a perspective of tensor completion. Namely, we convert future data recovery into a more inclusive problem called sequential tensor completion (STC), which is to recover a tensor of sequential structure from some entries sampled arbitrarily from the tensor. Unlike the ordinary tensor completion (OTC) problem studied in the majority of literature, STC has a distinctive setup that the target tensor is sequential and not permutable, which means that the target owns rich spatio-temporal structures. This enables the possibility of restoring the arbitrarily selected missing entries, which is not possible under the framework of OTC. Then we propose two methods to address STC, including Discrete Fourier Transform based $\ell_1$ minimization ($\mathrm{DFT}_{\ell_1}$) and Convolution Nuclear Norm Minimization (CNNM), where $\mathrm{DFT}_{\ell_1}$ is indeed a special case of CNNM. Whenever the target is low-rank in some convolution domain, CNNM provably succeeds in solving STC. This immediately gives that $\mathrm{DFT}_{\ell_1}$ is also successful when the Fourier transform of the target is sparse, as convolution low-rankness is a generalization of Fourier sparsity. Experiments on univariate time series, images and videos show encouraging results.

Question: Please tell me which class the center node 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)