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 "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"):
  Abstract: Momentum Stochastic Gradient Descent (MSGD) algorithm has been widely applied to many nonconvex optimization problems in machine learning. Popular examples include training deep neural networks, dimensionality reduction, and etc. Due to the lack of convexity and the extra momentum term, the optimization theory of MSGD is still largely unknown. In this paper, we study this fundamental optimization algorithm based on the so-called "strict saddle problem." By diffusion approximation type analysis, our study shows that the momentum helps escape from saddle points, but hurts the convergence within the neighborhood of optima (if without the step size annealing). Our theoretical discovery partially corroborates the empirical success of MSGD in training deep neural networks. Moreover, our analysis applies the martingale method and "Fixed-State-Chain" method from the stochastic approximation literature, which are of independent interest.

Includes members:
  Member "exploring generalization in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "deep hyperspherical learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural similarity learning")
  Member "accelerated gradient descent escapes saddle points faster than gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient descent with momentum to accelerate or to super accelerate")
  Member "dropping convexity for more efficient and scalable online multiview learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards understanding acceleration tradeoff between momentum and asynchrony in nonconvex stochastic optimization")
  Member "spurious local minima are common in two layer relu neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks")
  Member "online multiview representation learning dropping convexity for better efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards understanding acceleration tradeoff between momentum and asynchrony in nonconvex stochastic optimization")
  Member "matrix completion has no spurious local minimum":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "identity matters in deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "backward feature correction how deep learning performs deep learning")

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)