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: "stable low rank matrix recovery via null space properties"
  Abstract: The problem of recovering a matrix of low rank from an incomplete and possibly noisy set of linear measurements arises in a number of areas. In order to derive rigorous recovery results, the measurement map is usually modeled probabilistically. We derive sufficient conditions on the minimal amount of measurements ensuring recovery via convex optimization. We establish our results via certain properties of the null space of the measurement map. In the setting where the measurements are realized as Frobenius inner products with independent standard Gaussian random matrices we show that $10 r (n_1 + n_2)$ measurements are enough to uniformly and stably recover an $n_1 \times n_2$ matrix of rank at most $r$. We then significantly generalize this result by only requiring independent mean-zero, variance one entries with four finite moments at the cost of replacing $10$ by some universal constant. We also study the case of recovering Hermitian rank-$r$ matrices from measurement matrices proportional to rank-one projectors. For $m \geq C r n$ rank-one projective measurements onto independent standard Gaussian vectors, we show that nuclear norm minimization uniformly and stably reconstructs Hermitian rank-$r$ matrices with high probability. Next, we partially de-randomize this by establishing an analogous statement for projectors onto independent elements of a complex projective 4-designs at the cost of a slightly higher sampling rate $m \geq C rn \log n$. Moreover, if the Hermitian matrix to be recovered is known to be positive semidefinite, then we show that the nuclear norm minimization approach may be replaced by minimizing the $\ell_2$-norm of the residual subject to the positive semidefinite constraint. Then no estimate of the noise level is required a priori. We discuss applications in quantum physics and the phase retrieval problem.

Connected through hyperedge HE-1 (induced by source paper "an analysis of noise folding for low rank matrix recovery"):
  with members:
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "noise folding in compressed sensing"
    - "robust principal component analysis"
    - "a new approach to collaborative filtering operator estimation with spectral regularization"

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 "complex phase retrieval from subgaussian measurements"):
  with members:
    - "on the convex geometry of blind deconvolution and matrix completion"
    - "phase retrieval without small ball probability assumptions"
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "phasemax convex phase retrieval via basis pursuit"
    - "a geometric analysis of phase retrieval"
    - "phase retrieval meets statistical learning theory a flexible convex relaxation"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "solving complex quadratic systems with full rank random matrices"):
  with members:
    - "reconstructing point sets from distance distributions"
    - "linear spectral estimators and an application to phase retrieval"
    - "nonconvex matrix factorization from rank one measurements"
    - "coordinate descent algorithms for phase retrieval"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "generalized phase retrieval measurement number matrix recovery and beyond"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "phase retrieval via wirtinger flow theory and algorithms"

Connected through hyperedge HE-5 (induced by source paper "matrix recovery from rank one projection measurements via nonconvex minimization"):
  with members:
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressive multiplexing of correlated signals"
    - "sparse signal recovery from quadratic measurements via convex programming"
    - "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-6 (induced by source paper "derandomizing compressed sensing with combinatorial design"):
  with members:
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "low rank matrix recovery from clifford orbits"
    - "a partial derandomization of phaselift using spherical designs"
    - "learning without concentration"
    - "low rank matrix recovery from rank one measurements"
    - "restricted isometry of fourier matrices and list decodability of random linear codes"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "a derandomized sparse johnson lindenstrauss transform"

Connected through hyperedge HE-7 (induced by source paper "truncated sparse approximation property and truncated q norm minimization"):
  with members:
    - "nonlinear frames and sparse reconstructions in banach spaces"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"
    - "compressive multiplexing of correlated signals"
    - "compressed sensing and affine rank minimization under restricted isometry"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "sparse approximation property and stable recovery of sparse signals from noisy measurements"
    - "sparse signal reconstruction via iterative support detection"
    - "rank awareness in joint sparse recovery"

Connected through hyperedge HE-8 (induced by source paper "low rank positive semidefinite matrix recovery from corrupted rank one measurements"):
  with members:
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "compressive phase retrieval via generalized approximate message passing"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "solving quadratic equations via phaselift when there are about as many equations as unknowns"
    - "compressive principal component pursuit"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "compressed sensing and matrix completion with constant proportion of corruptions"

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)