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: "a unified approach to adaptive regularization in online and stochastic optimization"
  Abstract: We describe a framework for deriving and analyzing online optimization algorithms that incorporate adaptive, data-dependent regularization, also termed preconditioning. Such algorithms have been proven useful in stochastic optimization by reshaping the gradients according to the geometry of the data. Our framework captures and unifies much of the existing literature on adaptive online methods, including the AdaGrad and Online Newton Step algorithms as well as their diagonal versions. As a result, we obtain new convergence proofs for these algorithms that are substantially simpler than previous analyses. Our framework also exposes the rationale for the different preconditioned updates used in common stochastic optimization methods.

Connected through hyperedge HE-1 (induced by source paper "extreme tensoring for low memory preconditioning"):
  with members:
    - "introduction to online convex optimization"
    - "shampoo preconditioned stochastic tensor optimization"
    - "the case for full matrix adaptive regularization"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "learning neural networks with adaptive regularization"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "noisy natural gradient as variational inference"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "a bayesian variational framework for stochastic optimization"):
  with members:
    - "aggregated momentum stability through passive damping"
    - "a general system of differential equations to model first order adaptive algorithms"
    - "an overview of gradient descent optimization algorithms"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "predictor corrector policy optimization"):
  with members:
    - "introduction to online convex optimization"
    - "on the convergence of adam and beyond"
    - "universal planning networks"
    - "dual policy iteration"
    - "sim to real learning agile locomotion for quadruped robots"
    - "stochastic variance reduced policy gradient"
    - "convergence of value aggregation for imitation learning"
    - "accelerating imitation learning with predictive models"

Connected through hyperedge HE-5 (induced by source paper "shampoo preconditioned stochastic tensor optimization"):
  with members:
    - "introduction to online convex optimization"
    - "attention is all you need"
    - "understanding deep learning requires rethinking generalization"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "path sgd path normalized optimization in deep neural networks"
    - "faster sgd using sketched conditioning"
    - "adam a method for stochastic optimization"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-6 (induced by source paper "variants of rmsprop and adagrad with logarithmic regret bounds"):
  with members:
    - "introduction to online convex optimization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "an overview of gradient descent optimization algorithms"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "rethinking the inception architecture for computer vision"

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)