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: "deepobs a deep learning optimizer benchmark suite"
  Abstract: Because the choice and tuning of the optimizer affects the speed, and ultimately the performance of deep learning, there is significant past and recent research in this area. Yet, perhaps surprisingly, there is no generally agreed-upon protocol for the quantitative and reproducible evaluation of optimization strategies for deep learning. We suggest routines and benchmarks for stochastic optimization, with special focus on the unique aspects of deep learning, such as stochasticity, tunability and generalization. As the primary contribution, we present DeepOBS, a Python package of deep learning optimization benchmarks. The package addresses key challenges in the quantitative assessment of stochastic optimizers, and automates most steps of benchmarking. The library includes a wide and extensible set of ready-to-use realistic optimization problems, such as training Residual Networks for image classification on ImageNet or character-level language prediction models, as well as popular classics like MNIST and CIFAR-10. The package also provides realistic baseline results for the most popular optimizers on these test problems, ensuring a fair comparison to the competition when benchmarking new optimizers, and without having to run costly experiments. It comes with output back-ends that directly produce LaTeX code for inclusion in academic publications. It supports TensorFlow and is available open source.

Connected through hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "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"
    - "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"
    - "on empirical comparisons of optimizers for deep learning"
    - "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"
    - "reducing bert pre training time from 3 days to 76 minutes"

Connected through hyperedge HE-3 (induced by source paper "optimizer benchmarking needs to account for hyperparameter tuning"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "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-4 (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"
    - "on the convergence of adam and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "roberta a robustly optimized bert pretraining approach"
    - "ellipsoidal trust region methods and the marginal value of hessian information for neural network training"

Connected through hyperedge HE-5 (induced by source paper "training neural networks for and by interpolation"):
  with members:
    - "sgd converges to global minimum in deep learning via star convex path"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "deep frank wolfe for neural network optimization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "backpack packing more into backprop"):
  with members:
    - "a modular approach to block diagonal hessian approximations for second order optimization methods"
    - "limitations of the empirical fisher approximation"
    - "don t unroll adjoint differentiating ssa form programs"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "not all samples are created equal deep learning with importance sampling"
    - "dissecting adam the sign magnitude and variance of stochastic gradients"
    - "coupling adaptive batch sizes with learning rates"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "on the tunability of optimizers in deep learning"):
  with members:
    - "on empirical comparisons of optimizers for deep learning"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "modular block diagonal curvature approximations for feedforward architectures"):
  with members:
    - "the outer product structure of neural network derivatives"
    - "bda pch block diagonal approximation of positive curvature hessian for training neural networks"
    - "feedforward and recurrent neural networks backward propagation and hessian in matrix form"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"

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)