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 "information theoretic limits of matrix completion"):
  Abstract: We propose an information-theoretic framework for matrix completion. The theory goes beyond the low-rank structure and applies to general matrices of "low description complexity". Specifically, we consider $m\times n$ random matrices $\mathbf{X}$ of arbitrary distribution (continuous, discrete, discrete-continuous mixture, or even singular). With $\mathcal{S}$ an $\varepsilon$-support set of $\mathbf{X}$, i.e., $\mathrm{P}[\mathbf{X}\in\mathcal{S}]\geq 1-\varepsilon$, and $\underline{\mathrm{dim}}_\mathrm{B}(\mathcal{S})$ denoting the lower Minkowski dimension of $\mathcal{S}$, we show that $k> \underline{\mathrm{dim}}_\mathrm{B}(\mathcal{S})$ trace inner product measurements with measurement matrices $A_i$, suffice to recover $\mathbf{X}$ with probability of error at most $\varepsilon$. The result holds for Lebesgue a.a. $A_i$ and does not need incoherence between the $A_i$ and the unknown matrix $\mathbf{X}$. We furthermore show that $k> \underline{\mathrm{dim}}_\mathrm{B}(\mathcal{S})$ measurements also suffice to recover the unknown matrix $\mathbf{X}$ from measurements taken with rank-one $A_i$, again this applies to a.a. rank-one $A_i$. Rank-one measurement matrices are attractive as they require less storage space than general measurement matrices and can be applied faster. Particularizing our results to the recovery of low-rank matrices, we find that $k>(m+n-r)r$ measurements are sufficient to recover matrices of rank at most $r$. Finally, we construct a class of rank-$r$ matrices that can be recovered with arbitrarily small probability of error from $k<(m+n-r)r$ measurements.

Includes members:
  Member "almost lossless analog signal separation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "almost lossless analog compression without phase information")
  Member "sparse representation of a polytope and recovery of sparse signals and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the importance of phase in complex compressive sensing")
  Member "recovering low rank matrices from few coefficients in any basis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  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 "a simpler approach to matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "federated over the air subspace learning from incomplete data")
  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.IT(Information Theory)