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: "an overview of gradient descent optimization algorithms"
  Abstract: Gradient descent optimization algorithms, while increasingly popular, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This article aims to provide the reader with intuitions with regard to the behaviour of different algorithms that will allow her to put them to use. In the course of this overview, we look at different variants of gradient descent, summarize challenges, introduce the most common optimization algorithms, review architectures in a parallel and distributed setting, and investigate additional strategies for optimizing gradient descent.

Connected through hyperedge HE-1 (induced by source paper "hyper parameter optimization a review of algorithms and applications"):
  with members:
    - "a generalized framework for population based training"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "meta learning a survey"
    - "deep learning using rectified linear units relu"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "massively parallel hyperparameter tuning"
    - "combination of hyperband and bayesian optimization for hyperparameter optimization in deep learning"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "medical image analysis using convolutional neural networks a review"
    - "skin lesion segmentation u nets versus clustering"
    - "understanding convolution for semantic segmentation"
    - "a survey on deep learning in medical image analysis"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "the importance of skip connections in biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis"):
  with members:
    - "bandit principal component analysis"
    - "exponentially convergent stochastic k pca without variance reduction"
    - "an implicit form of krasulina s k pca update without the orthonormality constraint"
    - "on the regret minimization of nonconvex online gradient ascent for online pca"
    - "on the computational inefficiency of large batch sizes for stochastic gradient descent"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "follow the compressed leader faster online learning of eigenvectors and faster mmwu"

Connected through hyperedge HE-4 (induced by source paper "mga momentum gradient attack on network"):
  with members:
    - "unsupervised euclidean distance attack on network embedding"
    - "things you may not know about adversarial example a black box adversarial image attack"
    - "adversarial attack on graph structured data"
    - "adversarial training towards robust multimedia recommender system"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "poisoning attacks to graph based recommender systems"
    - "ga based q attack on community detection"
    - "adversarial examples that fool both computer vision and time limited humans"

Connected through hyperedge HE-5 (induced by source paper "kalman meets bellman improving policy evaluation through value tracking"):
  with members:
    - "a kernel loss for solving the bellman equation"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "directly estimating the variance of the lambda return using temporal difference methods"
    - "successor uncertainties exploration and uncertainty in temporal difference learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "shallow updates for deep reinforcement learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "the uncertainty bellman equation and exploration"

Connected through hyperedge HE-6 (induced by source paper "laprop a better way to combine momentum with adaptive gradient"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "gluoncv and gluonnlp deep learning in computer vision and natural language processing"
    - "optimization for deep learning theory and algorithms"
    - "on the variance of the adaptive learning rate and beyond"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "roberta a robustly optimized bert pretraining approach"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "optimize tsk fuzzy systems for classification problems mini batch gradient descent with uniform regularization and batch normalization"):
  with members:
    - "mixture models for diverse machine translation tricks of the trade"
    - "on the functional equivalence of tsk fuzzy systems to neural networks mixture of experts cart and stacking ensemble regression"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "group normalization"
    - "revisiting small batch training for deep neural networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "improving generalization performance by switching from adam to sgd"

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)