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 simpler approach to obtaining an o 1 t convergence rate for the projected stochastic subgradient method"
  Abstract: In this note, we present a new averaging technique for the projected stochastic subgradient method. By using a weighted average with a weight of t+1 for each iterate w_t at iteration t, we obtain the convergence rate of O(1/t) with both an easy proof and an easy implementation. The new scheme is compared empirically to existing techniques, with similar performance behavior.

Connected through hyperedge HE-1 (induced by source paper "simple and fast algorithm for binary integer and online linear programming"):
  with members:
    - "online linear programming dual convergence new algorithms and regret bounds"
    - "how good is sgd with random shuffling"
    - "introduction to online convex optimization"
    - "online convex optimization for cumulative constraints"
    - "a linearly relaxed approximate linear program for markov decision processes"
    - "fast algorithms for online stochastic convex programming"
    - "trading regret for efficiency online convex optimization with long term constraints"
    - "a dynamic near optimal algorithm for online linear programming"

Connected through hyperedge HE-2 (induced by source paper "revisiting sgd with increasingly weighted averaging optimization and generalization perspectives"):
  with members:
    - "a unified analysis of stochastic momentum methods for deep learning"
    - "deep residual learning for image recognition"
    - "train faster generalize better stability of stochastic gradient descent"
    - "adam a method for stochastic optimization"
    - "deep learning with elastic averaging sgd"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "a modern introduction to online learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "black box reductions for parameter free online learning in banach spaces"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "training deep networks without learning rates through coin betting"
    - "scale free online learning"
    - "coin betting and parameter free online learning"
    - "fighting bandits with a new kind of smoothness"
    - "second order quantile methods for experts and combinatorial games"

Connected through hyperedge HE-4 (induced by source paper "reducing the variance in online optimization by transporting past gradients"):
  with members:
    - "anytime tail averaging"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "learning dexterous in hand manipulation"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "global convergence of policy gradient methods for linearized control problems"
    - "measuring the effects of data parallelism on neural network training"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-5 (induced by source paper "robust neural network training using periodic sampling over model weights"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "simple baselines for human pose estimation and tracking"
    - "unified perceptual parsing for scene understanding"
    - "averaging weights leads to wider optima and better generalization"
    - "don t decay the learning rate increase the batch size"
    - "visualizing the loss landscape of neural nets"
    - "megdet a large mini batch object detector"
    - "improving generalization performance by switching from adam to sgd"

Connected through hyperedge HE-6 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"):
  with members:
    - "iterate averaging as regularization for stochastic gradient descent"
    - "tight analyses for non smooth stochastic gradient descent"
    - "how to make the gradients small stochastically"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"

Connected through hyperedge HE-7 (induced by source paper "learning to learn stochastic gradient descent with biased regularization"):
  with members:
    - "surrogate losses for online learning of stepsizes in stochastic non convex optimization"
    - "introduction to online convex optimization"
    - "fast rates by transferring from auxiliary hypotheses"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "a pac approach to application specific algorithm selection"
    - "sparse coding for multitask and transfer learning"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "the step decay schedule a near optimal geometrically decaying learning rate procedure"):
  with members:
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "tight analyses for non smooth stochastic gradient descent"
    - "how to make the gradients small stochastically"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"

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)