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: "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.

Connected through hyperedge HE-1 (induced by source paper "rate distortion theory for general sets and measures"):
  with members:
    - "the shannon lower bound is asymptotically tight"
    - "entropy and source coding for integer dimensional singular random variables"

Connected through hyperedge HE-2 (induced by source paper "optimal sample complexity for stable matrix recovery"):
  with members:
    - "an overview of low rank matrix recovery from incomplete observations"
    - "sketching for simultaneously sparse and low rank covariance matrices"
    - "identifiability and stability in blind deconvolution under minimal assumptions"
    - "blind recovery of sparse signals from subsampled convolution"
    - "living on the edge phase transitions in convex programs with random data"
    - "near optimal compressed sensing of a class of sparse low rank matrices via sparse power factorization"
    - "near optimal compressed sensing of sparse rank one matrices via sparse power factorization"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-3 (induced by source paper "a note on information theoretic bounds on matrix completion under union of subspaces model"):
  with members:
    - "on deterministic conditions for subspace clustering under missing data"
    - "recovering low rank matrices from few coefficients in any basis"
    - "near optimal column based matrix reconstruction"
    - "high rank matrix completion and subspace clustering with missing data"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-4 (induced by source paper "almost lossless analog compression without phase information"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "almost lossless analog signal separation"
    - "new conditions for sparse phase retrieval"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "phase retrieval stability and recovery guarantees"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "phase retrieval via matrix completion"

Connected through hyperedge HE-5 (induced by source paper "almost lossless analog signal separation and probabilistic uncertainty relations"):
  with members:
    - "almost lossless analog compression without phase information"
    - "almost lossless analog signal separation"
    - "uncertainty relations and sparse signal recovery for pairs of general signal sets"
    - "sharp recovery bounds for convex demixing with applications"
    - "probabilistic recovery guarantees for sparsely corrupted signals"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "recovery of sparsely corrupted signals"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "identifiability and stability in blind deconvolution under minimal assumptions"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration and biconvex compressive sensing"
    - "blind recovery of sparse signals from subsampled convolution"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "compressed subspace matching on the continuum"
    - "blind deconvolution using convex programming"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

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)