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 "closing the generalization gap of adaptive gradient methods in training deep neural networks"):
  Abstract: Adaptive gradient methods, which adopt historical gradient information to automatically adjust the learning rate, have been observed to generalize worse than stochastic gradient descent (SGD) with momentum in training deep neural networks. This leaves how to close the generalization gap of adaptive gradient methods an open problem. In this work, we show that adaptive gradient methods such as Adam, Amsgrad, are sometimes "over adapted". We design a new algorithm, called Partially adaptive momentum estimation method (Padam), which unifies the Adam/Amsgrad with SGD to achieve the best from both worlds. Experiments on standard benchmarks show that Padam can maintain fast convergence rate as Adam/Amsgrad while generalizing as well as SGD in training deep neural networks. These results would suggest practitioners pick up adaptive gradient methods once again for faster training of deep neural networks.

Includes members:
  Member "the step decay schedule a near optimal geometrically decaying learning rate procedure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards explaining the regularization effect of initial large learning rate in training neural networks")
  Member "adaptive gradient methods with dynamic bound of learning rate":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a metric learning reality check")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "understanding short horizon bias in stochastic meta optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "on the insufficiency of existing momentum schemes for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the two regimes of deep network training")
  Member "normalized direction preserving adam":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deam adaptive momentum with discriminative weight for stochastic optimization")
  Member "variants of rmsprop and adagrad with logarithmic regret bounds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compositional adam an adaptive compositional solver")
  Member "decoupled weight decay regularization":
    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)