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 "learning one hidden layer neural networks via provable gradient descent with random initialization"):
  Abstract: Although deep learning has shown its powerful performance in many applications, the mathematical principles behind neural networks are still mysterious. In this paper, we consider the problem of learning a one-hidden-layer neural network with quadratic activations. We focus on the under-parameterized regime where the number of hidden units is smaller than the dimension of the inputs. We shall propose to solve the problem via a provable gradient-based method with random initialization. For the non-convex neural networks training problem we reveal that the gradient descent iterates are able to enter a local region that enjoys strong convexity and smoothness within a few iterations, and then provably converges to a globally optimal model at a linear rate with near-optimal sample complexity. We further corroborate our theoretical findings via various experiments.

Includes members:
  Member "nonconvex demixing from bilinear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blind over the air computation and data fusion via provable wirtinger flow")
  Member "the global optimization geometry of shallow linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coherent gradients an approach to understanding generalization in gradient descent based optimization")
  Member "nonconvex matrix factorization from rank one measurements":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "solving complex quadratic systems with full rank random matrices")
  Member "blind over the air computation and data fusion via provable wirtinger flow":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal power control for over the air computation in fading channels")
  Member "a convergence analysis of gradient descent for deep linear neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a deep conditioning treatment of neural networks")
  Member "theoretical insights into the optimization landscape of over parameterized shallow neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "how to escape saddle points efficiently":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data")

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)