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 "gradient descent with momentum to accelerate or to super accelerate"):
  Abstract: We consider gradient descent with `momentum', a widely used method for loss function minimization in machine learning. This method is often used with `Nesterov acceleration', meaning that the gradient is evaluated not at the current position in parameter space, but at the estimated position after one step. In this work, we show that the algorithm can be improved by extending this `acceleration' --- by using the gradient at an estimated position several steps ahead rather than just one step ahead. How far one looks ahead in this `super-acceleration' algorithm is determined by a new hyperparameter. Considering a one-parameter quadratic loss function, the optimal value of the super-acceleration can be exactly calculated and analytically estimated. We show explicitly that super-accelerating the momentum algorithm is beneficial, not only for this idealized problem, but also for several synthetic loss landscapes and for the MNIST classification task with neural networks. Super-acceleration is also easy to incorporate into adaptive algorithms like RMSProp or Adam, and is shown to improve these algorithms.

Includes members:
  Member "calibrating the adaptive learning rate to improve convergence of adam":
    also connected through other hyperedges:
      (none)
  Member "on empirical comparisons of optimizers for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "decaying momentum helps neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep transfer learning based downlink channel prediction for fdd massive mimo systems")
  Member "a survey of optimization methods from a machine learning perspective":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compositional adam an adaptive compositional solver")
  Member "understanding the role of momentum in stochastic gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the convergence of nesterov s accelerated gradient method in stochastic settings")
  Member "adaptive gradient methods with dynamic bound of learning rate":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a metric learning reality check")
  Member "global convergence of adaptive gradient methods for an over parameterized neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convergence of end to end training in deep unsupervised contrasitive learning")
  Member "analysis of momentum methods":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochasticity of deterministic gradient descent large learning rate for multiscale objective function")

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)