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: "quasi hyperbolic momentum and adam for deep learning"
  Abstract: Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover. Finally, we propose a QH variant of Adam called QHAdam, and we empirically demonstrate that our algorithms lead to significantly improved training in a variety of settings, including a new state-of-the-art result on WMT16 EN-DE. We hope that these empirical results, combined with the conceptual and practical simplicity of QHM and QHAdam, will spur interest from both practitioners and researchers. Code is immediately available.

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 "flexible numerical optimization with ensmallen"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "practical sparse matrices in c with hybrid storage and template based expression optimisation"
    - "a user friendly hybrid sparse matrix class in c"
    - "ensmallen a flexible c library for efficient function optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "training gans with optimism"
    - "sgdr stochastic gradient descent with restarts"

Connected through hyperedge HE-3 (induced by source paper "deep differentiable forest with sparse attention for the tabular data"):
  with members:
    - "litemort a memory efficient gradient boosting tree system on adaptive compact distributions"
    - "neural oblivious decision ensembles for deep learning on tabular data"
    - "sparse sequence to sequence models"
    - "optimization methods for interpretable differentiable decision trees in reinforcement learning"
    - "deep neural decision trees"
    - "adaptive neural trees"
    - "catboost gradient boosting with categorical features support"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-4 (induced by source paper "on the convergence of nesterov s accelerated gradient method in stochastic settings"):
  with members:
    - "accelerating sgd with momentum for over parameterized learning"
    - "sgd general analysis and improved rates"
    - "understanding the role of momentum in stochastic gradient methods"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "negative momentum for improved game dynamics"
    - "on the curved geometry of accelerated optimization"
    - "linear coupling an ultimate unification of gradient and mirror descent"

Connected through hyperedge HE-5 (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-6 (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"
    - "aggregated momentum stability through passive damping"
    - "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-7 (induced by source paper "slowmo improving communication efficient distributed sgd with slow momentum"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "matcha speeding up decentralized sgd via matching decomposition sampling"
    - "understanding the role of momentum in stochastic gradient methods"
    - "lookahead optimizer k steps forward 1 step back"
    - "roberta a robustly optimized bert pretraining approach"
    - "decentralized deep learning with arbitrary communication compression"

Connected through hyperedge HE-8 (induced by source paper "on the adequacy of untuned warmup for adaptive optimization"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "on the variance of the adaptive learning rate and beyond"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "albert a lite bert for self supervised learning of language representations"
    - "roberta a robustly optimized bert pretraining approach"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "adaptive input representations for neural language modeling"

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)