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: "adaptive bound optimization for online convex optimization"
  Abstract: We introduce a new online convex optimization algorithm that adaptively chooses its regularization function based on the loss functions observed so far. This is in contrast to previous algorithms that use a fixed regularization function such as L2-squared, and modify it only via a single time-dependent parameter. Our algorithm's regret bounds are worst-case optimal, and for certain realistic classes of loss functions they are much better than existing bounds. These bounds are problem-dependent, which means they can exploit the structure of the actual problem instance. Critically, however, our algorithm does not need to know this structure in advance. Rather, we prove competitive guarantees that show the algorithm provides a bound within a constant factor of the best possible bound (of a certain functional form) in hindsight.

Connected through hyperedge HE-1 (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"
    - "deepobs a deep learning optimizer benchmark suite"

Connected through hyperedge HE-2 (induced by source paper "metaselector meta learning for recommendation with user level adaptive model selection"):
  with members:
    - "a closer look at few shot classification"
    - "neural graph collaborative filtering"
    - "meta learning pac bayes priors in model averaging"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "one at a time a meta learning recommender system for recommendation algorithm selection on micro level"
    - "federated meta learning with fast convergence and efficient communication"
    - "neural factorization machines for sparse predictive analytics"

Connected through hyperedge HE-3 (induced by source paper "adaptive federated optimization"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "federated learning challenges methods and future directions"
    - "on the convergence of adam and beyond"
    - "the non iid data quagmire of decentralized machine learning"

Connected through hyperedge HE-4 (induced by source paper "adaptive online learning with varying norms"):
  with members:
    - "extreme tensoring for low memory preconditioning"
    - "adaptive scale invariant online algorithms for learning linear models"
    - "parameter free online convex optimization with sub exponential noise"
    - "matrix free preconditioning in online learning"
    - "shampoo preconditioned stochastic tensor optimization"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"

Connected through hyperedge HE-5 (induced by source paper "is local sgd better than minibatch sgd"):
  with members:
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "unified optimal analysis of the stochastic gradient method"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"
    - "on the randomized complexity of minimizing a convex quadratic function"
    - "don t use large mini batches use local sgd"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"

Connected through hyperedge HE-6 (induced by source paper "lipschitz and comparator norm adaptivity in online learning"):
  with members:
    - "adaptive scale invariant online algorithms for learning linear models"
    - "introduction to online convex optimization"
    - "lipschitz adaptivity with multiple learning rates in online learning"
    - "black box reductions for parameter free online learning in banach spaces"
    - "online learning sufficient statistics and the burkholder method"
    - "scale invariant unconstrained online learning"
    - "zigzag a new approach to adaptive online learning"
    - "online learning without prior information"

Connected through hyperedge HE-7 (induced by source paper "local adaalter communication efficient stochastic gradient descent with adaptive learning rates"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"

Connected through hyperedge HE-8 (induced by source paper "blockwise adaptivity faster training and better generalization in deep learning"):
  with members:
    - "a sufficient condition for convergences of adam and rmsprop"
    - "block normalized gradient method an empirical study for training deep neural network"
    - "on the convergence of weighted adagrad with momentum for training deep neural networks"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"
    - "signsgd compressed optimisation for non convex problems"
    - "adashift decorrelation and convergence of adaptive learning rate methods"
    - "weighted adagrad with unified momentum"
    - "attention is all you need"

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)