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 learning rates and parallelization for stochastic sparse non smooth gradients"
  Abstract: Recent work has established an empirically successful framework for adapting learning rates for stochastic gradient descent (SGD). This effectively removes all needs for tuning, while automatically reducing learning rates over time on stationary problems, and permitting learning rates to grow appropriately in non-stationary tasks. Here, we extend the idea in three directions, addressing proper minibatch parallelization, including reweighted updates for sparse or orthogonal gradients, improving robustness on non-smooth loss functions, in the process replacing the diagonal Hessian estimation procedure that may not always be available by a robust finite-difference approximation. The final algorithm integrates all these components, has linear complexity and is hyper-parameter free.

Connected through hyperedge HE-1 (induced by source paper "tadam a robust stochastic gradient optimizer"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "robot learning in homes improving generalization and reducing dataset bias"
    - "a robust adaptive stochastic gradient method for deep learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "proximal policy optimization algorithms"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"

Connected through hyperedge HE-2 (induced by source paper "gradient energy matching for distributed asynchronous gradient descent"):
  with members:
    - "don t decay the learning rate increase the batch size"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "asynchronous methods for deep reinforcement learning"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "one weird trick for parallelizing convolutional neural networks"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "drmad distilling reverse mode automatic differentiation for optimizing hyperparameters of deep neural networks"):
  with members:
    - "scalable gradient based tuning of continuous regularization hyperparameters"
    - "deep residual learning for image recognition"
    - "automatic differentiation in machine learning a survey"
    - "qualitatively characterizing neural network optimization problems"
    - "the loss surfaces of multilayer networks"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "dynamics of stochastic gradient algorithms"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "adam a method for stochastic optimization"
    - "deep learning with elastic averaging sgd"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"
    - "practical recommendations for gradient based training of deep architectures"
    - "adadelta an adaptive learning rate method"
    - "stochastic gradient descent for non smooth optimization convergence results and optimal averaging schemes"
    - "towards optimal one pass large scale learning with averaged stochastic gradient descent"

Connected through hyperedge HE-5 (induced by source paper "adasecant robust adaptive secant method for stochastic gradient"):
  with members:
    - "generating sequences with recurrent neural networks"
    - "adadelta an adaptive learning rate method"
    - "theano new features and speed improvements"

Connected through hyperedge HE-6 (induced by source paper "unit tests for stochastic optimization"):
  with members:
    - "adadelta an adaptive learning rate method"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "on the difficulty of training recurrent neural networks"
    - "towards optimal one pass large scale learning with averaged stochastic gradient descent"

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)