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: "on empirical comparisons of optimizers for deep learning"
  Abstract: Selecting an optimizer is a central step in the contemporary deep learning pipeline. In this paper, we demonstrate the sensitivity of optimizer comparisons to the hyperparameter tuning protocol. Our findings suggest that the hyperparameter search space may be the single most important factor explaining the rankings obtained by recent empirical comparisons in the literature. In fact, we show that these results can be contradicted when hyperparameter search spaces are changed. As tuning effort grows without bound, more general optimizers should never underperform the ones they can approximate (i.e., Adam should never perform worse than momentum), but recent attempts to compare optimizers either assume these inclusion relationships are not practically relevant or restrict the hyperparameters in ways that break the inclusions. In our experiments, we find that inclusion relationships between optimizers matter in practice and always predict optimizer comparisons. In particular, we find that the popular adaptive gradient methods never underperform momentum or gradient descent. We also report practical tips around tuning often ignored hyperparameters of adaptive gradient methods and raise concerns about fairly benchmarking optimizers for neural network training.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "on the variance of the adaptive learning rate and beyond"
    - "the visual task adaptation benchmark"
    - "using learned optimizers to make models robust to input noise"
    - "deepobs a deep learning optimizer benchmark suite"
    - "nas bench 101 towards reproducible neural architecture search"
    - "on the tunability of optimizers in deep learning"

Connected through hyperedge HE-2 (induced by source paper "disentangling adaptive gradient methods from learning rates"):
  with members:
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"
    - "extreme tensoring for low memory preconditioning"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "escaping saddle points with adaptive gradient methods"
    - "an exponential learning rate schedule for deep learning"
    - "deepobs a deep learning optimizer benchmark suite"
    - "reducing bert pre training time from 3 days to 76 minutes"

Connected through hyperedge HE-3 (induced by source paper "fixmatch simplifying semi supervised learning with consistency and confidence"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "unsupervised data augmentation for consistency training"
    - "distribution density tails and outliers in machine learning metrics and applications"
    - "self training with noisy student improves imagenet classification"
    - "three mechanisms of weight decay regularization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "on the convergence of adam and beyond"

Connected through hyperedge HE-5 (induced by source paper "optimizer benchmarking needs to account for hyperparameter tuning"):
  with members:
    - "deepobs a deep learning optimizer benchmark suite"
    - "show your work improved reporting of experimental results"
    - "energy and policy considerations for deep learning in nlp"
    - "an empirical study on hyperparameter tuning of decision trees"
    - "measuring the effects of data parallelism on neural network training"
    - "on the state of the art of evaluation in neural language models"
    - "decoupled weight decay regularization"
    - "don t decay the learning rate increase the batch size"

Connected through hyperedge HE-6 (induced by source paper "on the tunability of optimizers in deep learning"):
  with members:
    - "deepobs a deep learning optimizer benchmark suite"
    - "an empirical study on hyperparameter tuning of decision trees"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "deep reinforcement learning that matters"
    - "on the state of the art of evaluation in neural language models"
    - "decoupled weight decay regularization"
    - "improving generalization performance by switching from adam to sgd"
    - "adam a method for stochastic 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)