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: "big batch sgd automated inference using adaptive batch sizes"
  Abstract: Classical stochastic gradient methods for optimization rely on noisy gradient approximations that become progressively less accurate as iterates approach a solution. The large noise and small signal in the resulting gradients makes it difficult to use them for adaptive stepsize selection and automatic stopping. We propose alternative "big batch" SGD schemes that adaptively grow the batch size over time to maintain a nearly constant signal-to-noise ratio in the gradient approximation. The resulting methods have similar convergence rates to classical SGD, and do not require convexity of the objective. The high fidelity gradients enable automated learning rate selection and do not require stepsize decay. Big batch methods are thus easily automated and can run with little or no oversight.

Connected through hyperedge HE-1 (induced by source paper "flexible numerical optimization with ensmallen"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "practical sparse matrices in c with hybrid storage and template based expression optimisation"
    - "a user friendly hybrid sparse matrix class in c"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "ensmallen a flexible c library for efficient function optimization"
    - "improving generalization performance by switching from adam to sgd"
    - "training gans with optimism"

Connected through hyperedge HE-2 (induced by source paper "painless stochastic gradient interpolation line search and convergence rates"):
  with members:
    - "training neural networks for and by interpolation"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "a variational inequality perspective on generative adversarial nets"
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "an alternative view when does sgd escape local minima"

Connected through hyperedge HE-3 (induced by source paper "data driven algorithm selection and parameter tuning two case studies in optimization and signal processing"):
  with members:
    - "oboe collaborative filtering for automl model selection"
    - "convolutional neural networks with alternately updated clique"
    - "learning to branch"
    - "adabatch efficient gradient aggregation rules for sequential and parallel stochastic gradient methods"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "learning to learn by gradient descent by gradient descent"
    - "densely connected convolutional networks"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"

Connected through hyperedge HE-4 (induced by source paper "fast and furious convergence stochastic second order methods under interpolation"):
  with members:
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "beyond least squares fast rates for regularized empirical risk minimization through self concordance"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "global linear convergence of newton s method without strong convexity or lipschitz gradients"
    - "on the acceleration of l bfgs with second order information and stochastic batches"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "adaptive newton method for empirical risk minimization to statistical accuracy"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "async a cloud engine with asynchrony and history for distributed machine learning"):
  with members:
    - "a distributed second order algorithm you can trust"
    - "speeding up distributed machine learning using codes"
    - "probabilistic synchronous parallel"
    - "revisiting distributed synchronous sgd"
    - "asynchronous distributed admm for large scale optimization part i algorithm and convergence analysis"
    - "web scale topic models in spark an asynchronous parameter server"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"

Connected through hyperedge HE-6 (induced by source paper "accelerating minibatch stochastic gradient descent using typicality sampling"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mentornet learning data driven curriculum for very deep neural networks on corrupted labels"
    - "learning what data to learn"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "importance sampling for minibatches"
    - "wavenet a generative model for raw audio"
    - "online batch selection for faster training of neural networks"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (induced by source paper "improving the convergence of sgd through adaptive batch sizes"):
  with members:
    - "don t decay the learning rate increase the batch size"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a bayesian perspective on generalization and stochastic gradient descent"
    - "understanding deep learning requires rethinking generalization"
    - "imagenet pre trained models with batch normalization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "coupling adaptive batch sizes with learning rates"

Connected through hyperedge HE-8 (induced by source paper "x traincaps accelerated training of capsule nets through lightweight software optimizations"):
  with members:
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "dynamic routing between capsules"
    - "adabatch adaptive batch sizes for training deep neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "starting small learning with adaptive sample sizes"
    - "sgdr stochastic gradient descent with restarts"
    - "stop wasting my gradients practical svrg"
    - "no more pesky learning rate guessing games"

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)