human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "run procrustes run on the convergence of accelerated procrustes flow"):
  Abstract: In this work, we present theoretical results on the convergence of non-convex accelerated gradient descent in matrix factorization models. The technique is applied to matrix sensing problems with squared loss, for the estimation of a rank $r$ optimal solution $X^\star \in \mathbb{R}^{n \times n}$. We show that the acceleration leads to linear convergence rate, even under non-convex settings where the variable $X$ is represented as $U U^\top$ for $U \in \mathbb{R}^{n \times r}$. Our result has the same dependence on the condition number of the objective --and the optimal solution-- as that of the recent results on non-accelerated algorithms. However, acceleration is observed in practice, both in synthetic examples and in two real applications: neuronal multi-unit activities recovery from single electrode recordings, and quantum state tomography on quantum computing simulators.

Includes members:
  Member "theoretical properties of the global optimizer of two layer neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the role of invariance in spectral complexity based generalization bounds")
  Member "spurious local minima are common in two layer relu neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "algorithmic regularization in over parameterized matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "revisiting landscape analysis in deep neural networks eliminating decreasing paths to infinity")
  Member "no spurious local minima in nonconvex low rank problems a unified geometric analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "no spurious local minima in deep quadratic networks")
  Member "guaranteed matrix completion via non convex factorization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "efficient approaches for escaping higher order saddle points in non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "necpd an online tensor decomposition with optimal stochastic gradient descent")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

Question: Please tell me which class the source paper 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)