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: "incoherence optimal matrix completion"
  Abstract: This paper considers the matrix completion problem. We show that it is not necessary to assume joint incoherence, which is a standard but unintuitive and restrictive condition that is imposed by previous studies. This leads to a sample complexity bound that is orderwise optimal with respect to the incoherence parameter (as well as to the rank    $r$    and the matrix dimension    $n$    up to a log factor). As a consequence, we improve the sample complexity of recovering a semidefinite matrix from    $O(nr^{2}\log ^{2}n)$    to    $O(nr\log ^{2}n)$   , and the highest allowable rank from    $\Theta (\sqrt {n}/\log n)$    to    $\Theta (n/\log ^{2}n)$   . The key step in proof is to obtain new bounds in terms of the    $\ell _{\infty ,2}$   -norm, defined as the maximum of the row and column norms of a matrix. To illustrate the applicability of our techniques, we discuss extensions to singular value decomposition projection, structured matrix completion and semisupervised clustering, for which we provide orderwise improvements over existing results. Finally, we turn to the closely related problem of low-rank-plus-sparse matrix decomposition. We show that the joint incoherence condition is unavoidable here for polynomial-time algorithms conditioned on the planted clique conjecture. This means it is intractable in general to separate a rank-   $\omega (\sqrt {n})$    positive semidefinite matrix and a sparse matrix. Interestingly, our results show that the standard and joint incoherence conditions are associated, respectively, with the information (statistical) and computational aspects of the matrix decomposition problem.

Connected through hyperedge HE-1 (induced by source paper "matrix completion with prior subspace information via maximizing correlation"):
  with members:
    - "optimal weighted low rank matrix recovery with subspace prior information"
    - "recovery of structured signals with prior information via maximizing correlation"
    - "weighted matrix completion and recovery with prior subspace information"
    - "provable inductive matrix completion"
    - "recovering low rank matrices from few coefficients in any basis"
    - "clustering partially observed graphs via convex optimization"
    - "learning with the weighted trace norm under arbitrary sampling distributions"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"

Connected through hyperedge HE-2 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion"):
  with members:
    - "phase retrieval without small ball probability assumptions"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "blind demixing and deconvolution at near optimal rate"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "low rank matrix recovery from clifford orbits"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-3 (induced by source paper "can agents learn by analogy an inferable model for pac reinforcement learning"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "sample optimal parametric q learning using linearly additive features"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "sample optimal parametric q learning with linear transition models"
    - "policy certificates towards accountable reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning of pomdp s using spectral methods"
    - "reinforcement learning in rich observation mdps using spectral methods"

Connected through hyperedge HE-4 (induced by source paper "average case lower bounds for learning sparse mixtures robust estimation and semirandom adversaries"):
  with members:
    - "how hard is robust mean estimation"
    - "optimal average case reductions to sparse pca from weak assumptions to strong hardness"
    - "computational hardness of certifying bounds on constrained pca problems"
    - "efficient algorithms for outlier robust regression"
    - "reducibility and computational lower bounds for problems with planted sparse structure"
    - "robust sparse estimation tasks in high dimensions"
    - "the power of sum of squares for detecting hidden structures"
    - "bayesian estimation from few samples community detection and related problems"

Connected through hyperedge HE-5 (induced by source paper "exact matrix completion based on low rank hankel structure in the fourier domain"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "blind demixing and deconvolution at near optimal rate"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "exact tensor completion using t svd"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "robust spectral compressed sensing via structured matrix completion"
    - "the achievable performance of convex demixing"

Connected through hyperedge HE-6 (induced by source paper "robust tensor completion using transformed tensor svd"):
  with members:
    - "exact tensor completion from sparsely corrupted observations via convex optimization"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "parallel matrix factorization for low rank tensor completion"
    - "exact tensor completion using t svd"
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "spectral unmixing via data guided sparsity"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

Connected through hyperedge HE-7 (induced by source paper "matrix completion with deterministic sampling theories and methods"):
  with members:
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "robust recovery of subspace structures by low rank representation"
    - "a combinatorial algebraic approach for the identifiability of low rank matrix completion"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-8 (induced by source paper "matrix completion with deterministic pattern a geometric perspective"):
  with members:
    - "on deterministic sampling patterns for robust low rank matrix completion"
    - "fundamental conditions for low cp rank tensor completion"
    - "rank determination for low rank data completion"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "practical sketching algorithms for low rank matrix approximation"
    - "guaranteed matrix completion via non convex factorization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recovering low rank matrices from few coefficients in any basis"

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)