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: "aggregated momentum stability through passive damping"
  Abstract: Momentum is a simple and widely used trick which allows gradient-based optimizers to pick up speed in low curvature directions. Its performance depends crucially on a damping coefficient $\beta$. Large $\beta$ values can potentially deliver much larger speedups, but are prone to oscillations and instability; hence one typically resorts to small values such as 0.5 or 0.9. We propose Aggregated Momentum (AggMo), a variant of momentum which combines multiple velocity vectors with different $\beta$ parameters. AggMo is trivial to implement, but significantly dampens oscillations, enabling it to remain stable even for aggressive $\beta$ values such as 0.999. We reinterpret Nesterov's accelerated gradient descent as a special case of AggMo and provide theoretical convergence bounds for online convex optimization. Empirically, we find that AggMo is a suitable drop-in replacement for other momentum methods, and frequently delivers faster convergence.

Connected through hyperedge HE-1 (induced by source paper "gradient descent with momentum to accelerate or to super accelerate"):
  with members:
    - "calibrating the adaptive learning rate to improve convergence of adam"
    - "on empirical comparisons of optimizers for deep learning"
    - "decaying momentum helps neural network training"
    - "a survey of optimization methods from a machine learning perspective"
    - "understanding the role of momentum in stochastic gradient methods"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "analysis of momentum methods"

Connected through hyperedge HE-2 (induced by source paper "decaying momentum helps neural network training"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "momentum based variance reduction in non convex sgd"
    - "on the convergence of adam and beyond"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "negative momentum for improved game dynamics"
    - "regularizing deep networks using efficient layerwise adversarial training"

Connected through hyperedge HE-3 (induced by source paper "using learned optimizers to make models robust to input noise"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "adversarial examples that fool both computer vision and time limited humans"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "adversarial spheres"
    - "adversarial vulnerability for any classifier"
    - "unsupervised learning via meta learning"

Connected through hyperedge HE-4 (induced by source paper "lookahead optimizer k steps forward 1 step back"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "reptile a scalable metalearning algorithm"
    - "averaging weights leads to wider optima and better generalization"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "adafactor adaptive learning rates with sublinear memory cost"

Connected through hyperedge HE-5 (induced by source paper "a bayesian variational framework for stochastic optimization"):
  with members:
    - "a general system of differential equations to model first order adaptive algorithms"
    - "a unified approach to adaptive regularization in online and stochastic optimization"
    - "an overview of gradient descent optimization algorithms"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "sorting out lipschitz function approximation"):
  with members:
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "the singular values of convolutional layers"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "on convergence and stability of gans"
    - "resurrecting the sigmoid in deep learning through dynamical isometry theory and practice"
    - "exploring generalization in deep learning"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-7 (induced by source paper "convergence guarantees for rmsprop and adam in non convex optimization and an empirical comparison to nesterov acceleration"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "on the state of the art of evaluation in neural language models"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "stronger baselines for trustable results in neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "understanding and correcting pathologies in the training of learned optimizers"):
  with members:
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "understanding short horizon bias in stochastic meta optimization"
    - "learning to optimize neural nets"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "unbiasing truncated backpropagation through time"
    - "evolved policy gradients"
    - "learning gradient descent better generalization and longer horizons"
    - "learned optimizers that scale and generalize"

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)