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: "rip based performance guarantee for low tubal rank tensor recovery"
  Abstract: The essential task of multi-dimensional data analysis focuses on the tensor decomposition and the corresponding notion of rank. In this paper, by introducing the notion of tensor singular value decomposition (t-SVD), we establish a regularized tensor nuclear norm minimization (RTNNM) model for low-tubal-rank tensor recovery. On the other hand, many variants of the restricted isometry property (RIP) have proven to be crucial frameworks and analysis tools for recovery of sparse vectors and low-rank tensors. So, we initiatively define a novel tensor restricted isometry property (t-RIP) based on t-SVD. Besides, our theoretical results show that any third-order tensor $\boldsymbol{\mathcal{X}}\in\mathbb{R}^{n_{1}\times n_{2}\times n_{3}}$ whose tubal rank is at most $r$ can stably be recovered from its as few as measurements $\boldsymbol{y} = \boldsymbol{\mathfrak{M}}(\boldsymbol{\mathcal{X}})+\boldsymbol{w}$ with a bounded noise constraint $\|\boldsymbol{w}\|_{2}\leq\epsilon$ via the RTNNM model, if the linear map $\boldsymbol{\mathfrak{M}}$ obeys t-RIP with \begin{equation*} #R##N#\delta_{tr}^{\boldsymbol{\mathfrak{M}}} 1$. Surprisingly, when $n_{3}=1$, our conditions coincide with Cai and Zhang's sharp work in 2013 for low-rank matrix recovery via the constrained nuclear norm minimization. We note that, as far as the authors are aware, such kind of result has not previously been reported in the literature.

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.IT(Information Theory)