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 "deam adaptive momentum with discriminative weight for stochastic optimization"):
  Abstract: Optimization algorithms with momentum, e.g., (ADAM), have been widely used for building deep learning models due to the faster convergence rates compared with stochastic gradient descent (SGD). Momentum helps accelerate SGD in the relevant directions in parameter updating, which can minify the oscillations of parameters update route. However, there exist errors in some update steps in optimization algorithms with momentum like ADAM. The fixed momentum weight (e.g., \beta_1 in ADAM) will propagate errors in momentum computing. In this paper, we introduce a novel optimization algorithm, namely Discriminative wEight on Adaptive Momentum (DEAM). Instead of assigning the momentum term weight with a fixed hyperparameter, DEAM proposes to compute the momentum weight automatically based on the discriminative angle. In this way, DEAM involves fewer hyperparameters. DEAM also contains a novel backtrack term, which restricts redundant updates when the correction of the last step is needed. Extensive experiments demonstrate that DEAM can achieve a faster convergence rate than the existing optimization algorithms in training the deep learning models of both convex and non-convex situations.

Includes members:
  Member "adaptive gradient methods with dynamic bound of learning rate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a metric learning reality check")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "graph neural networks for small graph and giant network representation learning an overview":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "graph neural distance metric learning with graph bert")
  Member "normalized direction preserving adam":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of optimization methods from a machine learning perspective")
  Member "on the convergence of a class of adam type algorithms for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling")
  Member "convergence analysis of proximal gradient with momentum for nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "investigating task driven latent feasibility for nonconvex image modeling")
  Member "improving generalization performance by switching from adam to sgd":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "an overview of gradient descent optimization algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hyper parameter optimization a review of algorithms and applications")

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)