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: "symmetry saddle points and global geometry of nonconvex matrix factorization"
  Abstract: We propose a general theory for studying the geometry of nonconvex objective functions with underlying symmetric structures. In specific, we characterize the locations of stationary points and the null space of the associated Hessian matrices via the lens of invariant groups. As a major motivating example, we apply the proposed general theory to characterize the global geometry of the low-rank matrix factorization problem. In particular, we illustrate how the rotational symmetry group gives rise to infinitely many non-isolated strict saddle points and equivalent global minima of the objective function. By explicitly identifying all stationary points, we divide the entire parameter space into three regions: ($\cR_1$) the region containing the neighborhoods of all strict saddle points, where the objective has negative curvatures; ($\cR_2$) the region containing neighborhoods of all global minima, where the objective enjoys strong convexity along certain directions; and ($\cR_3$) the complement of the above regions, where the gradient has sufficiently large magnitudes. We further extend our result to the matrix sensing problem. This allows us to establish strong global convergence guarantees for popular iterative algorithms with arbitrary initial solutions.

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"
    - "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-2 (induced by source paper "on the global convergence of imitation learning a case for linear quadratic regulator"):
  with members:
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "global convergence of policy gradient methods for linearized control problems"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "least squares temporal difference learning for the linear quadratic regulator"

Connected through hyperedge HE-3 (induced by source paper "noodl provable online dictionary learning and sparse coding"):
  with members:
    - "a dictionary based generalization of robust pca part ii applications to hyperspectral demixing"
    - "a dictionary based generalization of robust pca part i study of theoretical properties"
    - "target based hyperspectral demixing via generalized robust pca"
    - "stochastic variance reduced optimization for nonconvex sparse learning"
    - "a dictionary based generalization of robust pca"
    - "simple efficient and neural algorithms for sparse coding"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "dictionary learning and tensor decomposition via the sum of squares method"

Connected through hyperedge HE-4 (induced by source paper "on the sample complexity of actor critic method for reinforcement learning with function approximation"):
  with members:
    - "finite sample analysis for sarsa and q learning with linear function approximation"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "stochastic variance reduced policy gradient"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "finite sample analysis of two timescale stochastic approximation with applications to reinforcement learning"
    - "asynchronous methods for deep reinforcement learning"
    - "a geometric analysis of phase retrieval"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-5 (induced by source paper "online convex matrix factorization with representative regions"):
  with members:
    - "a ranking model motivated by nonnegative matrix factorization with applications to tennis tournaments"
    - "sparse approximation via generating point sets"
    - "simultaneous codeword optimization simco for dictionary update and learning"
    - "convex sparse matrix factorizations"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-6 (induced by source paper "improved learning of one hidden layer convolutional neural networks with overlaps"):
  with members:
    - "learning one convolutional layer with overlapping patches"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"

Connected through hyperedge HE-7 (induced by source paper "the global optimization geometry of shallow linear neural networks"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "deep linear neural networks with arbitrary loss all local minima are global"
    - "depth creates no bad local minima"
    - "global optimality conditions for deep neural networks"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "global optimality in low rank matrix optimization"

Connected through hyperedge HE-8 (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"
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "the global optimization geometry of low rank matrix optimization"
    - "fast algorithms for robust pca via gradient descent"

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)