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: "recovery guarantee of weighted low rank approximation via alternating minimization"
  Abstract: Many applications require recovering a ground truth low-rank matrix from noisy observations of the entries. In practice, this is typically formulated as weighted low-rank approximation problem and solved using non-convex optimization heuristics such as alternating minimization. Such non-convex techniques have little guarantees. Even worse, weighted low-rank approximation is NP-hard for even the most simple case when the ground truth is a rank-1 matrix. #R##N#In this paper, we provide provable recovery guarantee in polynomial time for a natural class of matrices and weights. In particular, we bound the spectral norm of the difference between the recovered matrix and the ground truth, by the spectral norm of the weighted noise plus an additive error term that decreases exponentially with the number of rounds of alternating minimization. This provides the first theoretical result for weighted low-rank approximation via alternating minimization with non-binary deterministic weights. It is a significant generalization of the results for matrix completion, the special case with binary weights, since our assumptions are similar or weaker than those made in existing works. #R##N#The key technical challenge is that under non-binary deterministic weights, naive alternating minimization steps will destroy the incoherency and spectral properties of the intermediate solution, which are needed for making progress towards the ground truth. One of our key technical contributions is a whitening step that maintains these properties of the intermediate solution after each round, which may be applied to alternating minimization for other problems and thus is of independent interest.

Connected through hyperedge HE-1 (induced by source paper "weighted matrix completion from non random non uniform sampling patterns"):
  with members:
    - "matrix completion with deterministic pattern a geometric perspective"
    - "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"
    - "matrix completion via max norm constrained optimization"
    - "weighted matrix completion and recovery with prior subspace information"
    - "mc 2 a two phase algorithm for leveraged matrix completion"
    - "tighter low rank approximation via sampling the leveraged element"

Connected through hyperedge HE-2 (induced by source paper "regularized weighted low rank approximation"):
  with members:
    - "practical sketching algorithms for low rank matrix approximation"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "optimal approximate matrix product in terms of stable rank"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "condition numbers of gaussian random matrices"

Connected through hyperedge HE-3 (induced by source paper "non convex matrix completion against a semi random adversary"):
  with members:
    - "how to escape saddle points efficiently"
    - "matrix completion has no spurious local minimum"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"
    - "deep learning in neural networks"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "how robust are reconstruction thresholds for community detection"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-4 (induced by source paper "a direct method to learn states and parameters of ordinary differential equations"):
  with members:
    - "convergence analysis of proximal gradient with momentum for nonconvex optimization"

Connected through hyperedge HE-5 (induced by source paper "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "non convex phase retrieval from stft measurements"
    - "a non convex blind calibration method for randomised sensing strategies"
    - "fast algorithms for robust pca via gradient descent"
    - "phase retrieval using gauss newton method"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-6 (induced by source paper "optimal sample complexity for matrix completion and related problems via 120001 s 2 regularization"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "how to escape saddle points efficiently"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "incoherence optimal matrix completion"

Connected through hyperedge HE-7 (induced by source paper "no spurious local minima in nonconvex low rank problems a unified geometric analysis"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "how to escape saddle points efficiently"
    - "matrix completion has no spurious local minimum"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "basis learning as an algorithmic primitive"
    - "eigenvectors of orthogonally decomposable functions"

Connected through hyperedge HE-8 (induced by source paper "matrix completion has no spurious local minimum"):
  with members:
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "fast exact matrix completion with finite samples"
    - "fast matrix completion without the condition number"
    - "understanding alternating minimization for matrix completion"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "matrix completion from noisy entries"
    - "a simpler approach to matrix completion"

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.LG(Machine Learning)