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: "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
  Abstract: We study the problem of estimating a low-rank positive semidefinite (PSD) matrix from a set of rank-one measurements using sensing vectors composed of i.i.d. standard Gaussian entries, which are possibly corrupted by arbitrary outliers. This problem arises from applications, such as phase retrieval, covariance sketching, quantum space tomography, and power spectrum estimation. We first propose a convex optimization algorithm that seeks the PSD matrix with the minimum   $\ell _1$  -norm of the observation residual. The advantage of our algorithm is that it is free of parameters, therefore, eliminating the need for tuning parameters and allowing easy implementations. We establish that with high probability, a low-rank PSD matrix can be exactly recovered as soon as the number of measurements is large enough, even when a fraction of the measurements are corrupted by outliers with arbitrary magnitudes. Moreover, the recovery is also stable against bounded noise. With the additional information of an upper bound of the rank of the PSD matrix, we propose another nonconvex algorithm based on subgradient descent that demonstrates excellent empirical performance in terms of computational efficiency and accuracy.

Connected through hyperedge HE-1 (induced by source paper "the non convex geometry of low rank matrix optimization"):
  with members:
    - "global optimality in low rank matrix optimization"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "the global optimization geometry of low rank matrix optimization"
    - "overcomplete tensor decomposition via convex optimization"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-2 (induced by source paper "nonconvex robust low rank matrix recovery"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "the non convex geometry of low rank matrix optimization"
    - "subgradient descent learns orthogonal dictionaries"
    - "global optimality in low rank matrix optimization"
    - "nonconvex low rank matrix recovery with arbitrary outliers via median truncated gradient descent"
    - "the global optimization geometry of low rank matrix optimization"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-3 (induced by source paper "matrix recovery from rank one projection measurements via nonconvex minimization"):
  with members:
    - "stable low rank matrix recovery via null space properties"
    - "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-4 (induced by source paper "nonconvex matrix factorization from rank one measurements"):
  with members:
    - "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "a geometric analysis of phase retrieval"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "low rank matrix recovery from rank one measurements"

Connected through hyperedge HE-5 (induced by source paper "nonconvex low rank matrix recovery with arbitrary outliers via median truncated gradient descent"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "how to escape saddle points efficiently"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "matrix completion has no spurious local minimum"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "nearly optimal robust matrix completion"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"

Connected through hyperedge HE-6 (induced by source paper "global optimality in low rank matrix optimization"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "geometry of factored nuclear norm regularization"
    - "the global optimization geometry of low rank matrix optimization"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"

Connected through hyperedge HE-7 (induced by source paper "sparse phase retrieval via truncated amplitude flow"):
  with members:
    - "channels with cooperation links that may be absent"
    - "sublinear time algorithms for compressive phase retrieval"
    - "fundamental limits of phasemax for phase retrieval a replica analysis"
    - "undersampled sparse phase retrieval via majorization minimization"
    - "scalable solvers of random quadratic equations via stochastic truncated amplitude flow"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "non convex phase retrieval from stft measurements"
    - "phase retrieval using feasible point pursuit algorithms and cramer rao bound"

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)