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: "hot swapping for online adaptation of optimization hyperparameters"
  Abstract: We describe a general framework for online adaptation of optimization hyperparameters by `hot swapping' their values during learning. We investigate this approach in the context of adaptive learning rate selection using an explore-exploit strategy from the multi-armed bandit literature. Experiments on a benchmark neural network show that the hot swapping approach leads to consistently better solutions compared to well-known alternatives such as AdaDelta and stochastic gradient with exhaustive hyperparameter search.

Connected through hyperedge HE-1 (induced by source paper "demystifying learning rate polices for high accuracy training of deep neural networks"):
  with members:
    - "a comparative measurement study of deep learning as a service framework"
    - "super convergence very fast training of residual networks using large learning rates"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "channel pruning for accelerating very deep neural networks"
    - "sgdr stochastic gradient descent with restarts"
    - "an overview of gradient descent optimization algorithms"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "no more pesky learning rate guessing games"):
  with members:
    - "sgdr stochastic gradient descent with restarts"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "an empirical evaluation of deep learning on highway driving"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "equilibrated adaptive learning rates for non convex optimization"

Connected through hyperedge HE-3 (induced by source paper "cyclical learning rates for training neural networks"):
  with members:
    - "sgdr stochastic gradient descent with restarts"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "an overview of gradient descent optimization algorithms"
    - "an empirical evaluation of deep learning on highway driving"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)