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: "provable efficient online matrix completion via non convex stochastic gradient descent"
  Abstract: Matrix completion, where we wish to recover a low rank matrix by observing a few entries from it, is a widely studied problem in both theory and practice with wide applications. Most of the provable algorithms so far on this problem have been restricted to the offline setting where they provide an estimate of the unknown matrix using all observations simultaneously. However, in many applications, the online version, where we observe one entry at a time and dynamically update our estimate, is more appealing. While existing algorithms are efficient for the offline setting, they could be highly inefficient for the online setting. #R##N#In this paper, we propose the first provable, efficient online algorithm for matrix completion. Our algorithm starts from an initial estimate of the matrix and then performs non-convex stochastic gradient descent (SGD). After every observation, it performs a fast update involving only one row of two tall matrices, giving near linear total runtime. Our algorithm can be naturally used in the offline setting as well, where it gives competitive sample complexity and runtime to state of the art algorithms. Our proofs introduce a general framework to show that SGD updates tend to stay away from saddle surfaces and could be of broader interests for other non-convex problems to prove tight rates.

Connected through hyperedge HE-1 (induced by source paper "understanding the downstream instability of word embeddings"):
  with members:
    - "on the downstream performance of compressed word embeddings"
    - "the influence of down sampling strategies on svd word embedding stability"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "factors influencing the surprising instability of word embeddings"
    - "on the dimensionality of word embedding"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "on the convergence of stochastic gradient descent with low rank projections for convex low rank matrix problems"):
  with members:
    - "fast stochastic algorithms for low rank and nonsmooth matrix problems"
    - "fast algorithms for robust pca via gradient descent"
    - "variance reduced and projection free stochastic optimization"
    - "matrix completion has no spurious local minimum"
    - "projection free online learning"
    - "phase retrieval via matrix completion"
    - "making gradient descent optimal for strongly convex stochastic optimization"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-3 (induced by source paper "online high rank matrix completion"):
  with members:
    - "guaranteed matrix completion via non convex factorization"
    - "online matrix completion and online robust pca"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "understanding alternating minimization for matrix completion"
    - "sparse subspace clustering algorithm theory and applications"
    - "online identification and tracking of subspaces from highly incomplete information"
    - "matrix completion from a few entries"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (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"
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "provable subspace tracking from missing data and matrix completion"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "subspace estimation from incomplete observations a high dimensional analysis"
    - "static and dynamic robust pca and matrix completion a review"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-6 (induced by source paper "fast exact matrix completion a unifying optimization framework"):
  with members:
    - "don t decay the learning rate increase the batch size"
    - "fast exact matrix completion with finite samples"
    - "provable inductive matrix completion"
    - "clustering partially observed graphs via convex optimization"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "the power of convex relaxation near optimal matrix completion"
    - "guaranteed rank minimization via singular value projection"
    - "matrix completion from a few entries"

Connected through hyperedge HE-7 (induced by source paper "subspace estimation from incomplete observations a high dimensional analysis"):
  with members:
    - "scaling limit exact and tractable analysis of online learning algorithms with applications to regularized regression and pca"
    - "the scaling limit of high dimensional online independent component analysis"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "online learning for sparse pca in high dimensions exact dynamics and phase transitions"
    - "convergence of a grassmannian gradient descent algorithm for subspace estimation from undersampled data"
    - "online matrix completion and online robust pca"
    - "global convergence of a grassmannian gradient descent algorithm for subspace estimation"
    - "convergence of stochastic gradient descent for pca"

Connected through hyperedge HE-8 (induced by source paper "on the regret minimization of nonconvex online gradient ascent for online pca"):
  with members:
    - "introduction to online convex optimization"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "matrix completion has no spurious local minimum"
    - "convergence of stochastic gradient descent for pca"
    - "the fast convergence of incremental pca"
    - "provable bounds for learning some deep representations"
    - "near optimal algorithms for online matrix prediction"

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)