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: "starting small learning with adaptive sample sizes"
  Abstract: For many machine learning problems, data is abundant and it may be prohibitive to make multiple passes through the full training set. In this context, we investigate strategies for dynamically increasing the effective sample size, when using iterative methods such as stochastic gradient descent. Our interest is motivated by the rise of variance-reduced methods, which achieve linear convergence rates that scale favorably for smaller sample sizes. Exploiting this feature, we show -- theoretically and empirically -- how to obtain significant speed-ups with a novel algorithm that reaches statistical accuracy on an $n$-sample in $2n$, instead of $n \log n$ steps.

Connected through hyperedge HE-1 (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"
    - "sgdr stochastic gradient descent with restarts"
    - "big batch sgd automated inference using adaptive batch sizes"
    - "stop wasting my gradients practical svrg"
    - "no more pesky learning rate guessing games"

Connected through hyperedge HE-2 (induced by source paper "approximate newton based statistical inference using only stochastic gradients"):
  with members:
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "efficient representation of low dimensional manifolds using deep networks"
    - "deep nets for local manifold learning"
    - "stop wasting my gradients practical svrg"
    - "online batch selection for faster training of neural networks"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-3 (induced by source paper "sub sampled cubic regularization for non convex optimization"):
  with members:
    - "variance reduced stochastic gradient descent with neighbors"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "the loss surfaces of multilayer networks"
    - "a linear time algorithm for trust region problems"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "most tensor problems are np hard"

Connected through hyperedge HE-4 (induced by source paper "accelerated dual learning by homotopic initialization"):
  with members:
    - "adaptive newton method for empirical risk minimization to statistical accuracy"
    - "data dependent initializations of convolutional neural networks"
    - "matrix completion from a few entries"

Connected through hyperedge HE-5 (induced by source paper "first order adaptive sample size methods to reduce complexity of empirical risk minimization"):
  with members:
    - "adaptive newton method for empirical risk minimization to statistical accuracy"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-6 (induced by source paper "dynanewton accelerating newton s method for machine learning"):
  with members:
    - "reducing runtime by recycling samples"
    - "on graduated optimization for stochastic non convex problems"
    - "variance reduced stochastic gradient descent with neighbors"
    - "res regularized stochastic bfgs algorithm"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-7 (induced by source paper "adaptive newton method for empirical risk minimization to statistical accuracy"):
  with members:
    - "res regularized stochastic bfgs algorithm"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

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)