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 "on the convergence of nesterov s accelerated gradient method in stochastic settings"):
  Abstract: We study Nesterov's accelerated gradient method in the stochastic approximation setting (unbiased gradients with bounded variance) and the finite-sum setting (where randomness is due to sampling mini-batches). To build better insight into the behavior of Nesterov's method in stochastic settings, we focus throughout on objectives that are smooth, strongly-convex, and twice continuously differentiable. In the stochastic approximation setting, Nesterov's method converges to a neighborhood of the optimal point at the same accelerated rate as in the deterministic setting. Perhaps surprisingly, in the finite-sum setting, we prove that Nesterov's method may diverge with the usual choice of step-size and momentum, unless additional conditions on the problem related to conditioning and data coherence are satisfied. Our results shed light as to why Nesterov's method may fail to converge or achieve acceleration in the finite-sum setting.

Includes members:
  Member "accelerating sgd with momentum for over parameterized learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "toward a theory of optimization for over parameterized systems of non linear equations the lessons of deep learning")
  Member "sgd general analysis and improved rates":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "federated learning of a mixture of global and local models")
  Member "understanding the role of momentum in stochastic gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient descent with momentum to accelerate or to super accelerate")
  Member "on the insufficiency of existing momentum schemes for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the two regimes of deep network training")
  Member "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "toward a theory of optimization for over parameterized systems of non linear equations the lessons of deep learning")
  Member "quasi hyperbolic momentum and adam for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gradient descent with momentum to accelerate or to super accelerate")
  Member "negative momentum for improved game dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from poincar e recurrence to convergence in imperfect information games finding equilibrium via regularization")
  Member "on the curved geometry of accelerated optimization":
    also connected through other hyperedges:
      (none)

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)