human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "nostalgic adam weighing more of the past gradients when designing the adaptive learning rate"
  Abstract: First-order optimization methods have been playing a prominent role in deep learning. Algorithms such as RMSProp and Adam are rather popular in training deep neural networks on large datasets. Recently, Reddi et al. discovered a flaw in the proof of convergence of Adam, and the authors proposed an alternative algorithm, AMSGrad, which has guaranteed convergence under certain conditions. In this paper, we propose a new algorithm, called Nostalgic Adam (NosAdam), which places bigger weights on the past gradients than the recent gradients when designing the adaptive learning rate. This is a new observation made through mathematical analysis of the algorithm. We also show that the estimate of the second moment of the gradient in NosAdam vanishes slower than Adam, which may account for faster convergence of NosAdam. We analyze the convergence of NosAdam and discover a convergence rate that achieves the best known convergence rate $O(1/\sqrt{T})$ for general convex online learning problems. Empirically, we show that NosAdam outperforms AMSGrad and Adam in some common machine learning problems.

Connected through hyperedge HE-1 (induced by source paper "diffgrad an optimization method for convolutional neural networks"):
  with members:
    - "spontaneous facial micro expression recognition using 3d spatiotemporal convolutional neural networks"
    - "on the convergence of adam and beyond"
    - "predictive local smoothness for stochastic gradient methods"
    - "path aggregation network for instance segmentation"
    - "signsgd compressed optimisation for non convex problems"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "a sufficient condition for convergences of adam and rmsprop"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "adashift decorrelation and convergence of adaptive learning rate methods"
    - "weighted adagrad with unified momentum"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "variants of rmsprop and adagrad with logarithmic regret bounds"

Connected through hyperedge HE-3 (induced by source paper "on the convergence of a class of adam type algorithms for non convex optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "weighted adagrad with unified momentum"
    - "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "equilibrated adaptive learning rates for non convex optimization"

Question: Please tell me which class the center node 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)