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: "adasecant robust adaptive secant method for stochastic gradient"
  Abstract: Stochastic gradient algorithms have been the main focus of large-scale learning problems and they led to important successes in machine learning. The convergence of SGD depends on the careful choice of learning rate and the amount of the noise in stochastic estimates of the gradients. In this paper, we propose a new adaptive learning rate algorithm, which utilizes curvature information for automatically tuning the learning rates. The information about the element-wise curvature of the loss function is estimated from the local statistics of the stochastic first order gradients. We further propose a new variance reduction technique to speed up the convergence. In our preliminary experiments with deep neural networks, we obtained better performance compared to the popular stochastic gradient algorithms.

Connected through hyperedge HE-1 (induced by source paper "super convergence very fast training of residual networks using large learning rates"):
  with members:
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "three factors influencing minima in sgd"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "don t decay the learning rate increase the batch size"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "a robust adaptive stochastic gradient method for deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a bayesian perspective on generalization and stochastic gradient descent"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive study of deep bidirectional lstm rnns for acoustic modeling in speech recognition"):
  with members:
    - "lstm a search space odyssey"
    - "associative long short term memory"
    - "returnn the rwth extensible training framework for universal recurrent neural networks"
    - "deep learning in neural networks"
    - "deep recurrent neural networks for acoustic modelling"
    - "benchmarking of lstm networks"
    - "deep residual learning for image recognition"
    - "highway long short term memory rnns for distant speech recognition"

Connected through hyperedge HE-3 (induced by source paper "a robust adaptive stochastic gradient method for deep learning"):
  with members:
    - "adam a method for stochastic optimization"
    - "theano new features and speed improvements"

Connected through hyperedge HE-4 (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-5 (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)