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: "global optimality in low rank matrix optimization"
  Abstract: This paper considers the minimization of a general objective function    $f(\boldsymbol{X})$   over the set of rectangular    $n\times m$   matrices that have rank at most   $r$   . To reduce the computational burden, we factorize the variable    $\boldsymbol{X}$   into a product of two smaller matrices and optimize over these two matrices instead of   $\boldsymbol{X}$   . Despite the resulting nonconvexity, recent studies in matrix completion and sensing have shown that the factored problem has no spurious local minima and obeys the so-called strict saddle property (the function has a directional negative curvature at all critical points but local minima). We analyze the global geometry for a general and yet well-conditioned objective function   $f(\boldsymbol{X})$    whose restricted strong convexity and restricted strong smoothness constants are comparable. In particular, we show that the reformulated objective function has no spurious local minima and obeys the strict saddle property. These geometric properties imply that a number of iterative optimization algorithms (such as gradient descent) can provably solve the factored problem with global convergence.

Connected through hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "nonconvex robust low rank matrix recovery"
    - "dual principal component pursuit probability analysis and efficient algorithms"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"

Connected through hyperedge HE-2 (induced by source paper "fair principal component analysis and filter design"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "efficient fair principal component analysis"
    - "fair dimensionality reduction and iterative rounding for sdps"
    - "convex formulations for fair principal component analysis"
    - "the price of fair pca one extra dimension"
    - "robust computation of linear models by convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "the non convex geometry of low rank matrix optimization"):
  with members:
    - "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"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-4 (induced by source paper "collaborative self attention for recommender systems"):
  with members:
    - "convolutional geometric matrix completion"
    - "outer product based neural collaborative filtering"
    - "multi pointer co attention networks for recommendation"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "deep semantic role labeling with self attention"
    - "scalable robust matrix factorization with nonconvex loss"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"

Connected through hyperedge HE-5 (induced by source paper "sharp restricted isometry bounds for the inexistence of spurious local minima in nonconvex matrix recovery"):
  with members:
    - "how much restricted isometry is needed in nonconvex matrix recovery"
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "phasemax convex phase retrieval via basis pursuit"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "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-6 (induced by source paper "on the convergence of perturbed distributed asynchronous stochastic gradient descent to second order stationary points in non convex optimization"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "taming convergence for asynchronous stochastic gradient descent with unbounded delay in non convex learning"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "matrix completion has no spurious local minimum"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "deep learning with elastic averaging sgd"
    - "nomad non locking stochastic multi machine algorithm for asynchronous and decentralized matrix completion"

Connected through hyperedge HE-7 (induced by source paper "loss landscapes of regularized linear autoencoders"):
  with members:
    - "deep learning without weight transport"
    - "on the implicit bias of dropout"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-8 (induced by source paper "spherical principal component analysis"):
  with members:
    - "the non convex geometry of low rank matrix optimization"

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)