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: "adashift decorrelation and convergence of adaptive learning rate methods"
  Abstract: Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, but their efficiency turns out to be unsatisfactory in practice. In this paper, we provide a new insight into the non-convergence issue of Adam as well as other adaptive learning rate methods. We argue that there exists an inappropriate correlation between gradient $g_t$ and the second moment term $v_t$ in Adam ($t$ is the timestep), which results in that a large gradient is likely to have small step size while a small gradient may have a large step size. We demonstrate that such unbalanced step sizes are the fundamental cause of non-convergence of Adam, and we further prove that decorrelating $v_t$ and $g_t$ will lead to unbiased step size for each gradient, thus solving the non-convergence problem of Adam. Finally, we propose AdaShift, a novel adaptive learning rate method that decorrelates $v_t$ and $g_t$ by temporal shifting, i.e., using temporally shifted gradient $g_{t-n}$ to calculate $v_t$. The experiment results demonstrate that AdaShift is able to address the non-convergence issue of Adam, while still maintaining a competitive performance with Adam in terms of both training speed and generalization.

Connected through hyperedge HE-1 (induced by source paper "on empirical comparisons of optimizers for deep learning"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "on the variance of the adaptive learning rate and beyond"
    - "namsg an efficient method for training neural networks"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "deepobs a deep learning optimizer benchmark suite"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-2 (induced by source paper "blockwise adaptivity faster training and better generalization in deep learning"):
  with members:
    - "a sufficient condition for convergences of adam and rmsprop"
    - "block normalized gradient method an empirical study for training deep neural network"
    - "on the convergence of weighted adagrad with momentum for training deep neural networks"
    - "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"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-3 (induced by source paper "domain independent dominance of adaptive methods"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "on the convergence of adabound and its connection to sgd"
    - "an analysis of neural language modeling at multiple scales"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "attention is all you need"
    - "decoupled weight decay regularization"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-4 (induced by source paper "an adaptive and momental bound method for stochastic learning"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fixup initialization residual learning without normalization"
    - "on the convergence of adam and beyond"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "towards efficient and unbiased implementation of lipschitz continuity in gans"):
  with members:
    - "lipschitz generative adversarial nets"
    - "on the convergence of adam and beyond"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "banach wasserstein gan"
    - "on convergence and stability of gans"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "does adam optimizer keep close to the optimal point"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "an alternative view when does sgd escape local minima"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "convergence analysis of two layer neural networks with relu activation"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-7 (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"
    - "weighted adagrad with unified momentum"
    - "nostalgic adam weighing more of the past gradients when designing the adaptive learning rate"
    - "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-8 (induced by source paper "nostalgic adam weighing more of the past gradients when designing the adaptive learning rate"):
  with members:
    - "on the convergence of adam and beyond"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "visualizing the loss landscape of neural nets"
    - "improving generalization performance by switching from adam to sgd"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"

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)