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: "stochastic gradient descent with biased but consistent gradient estimators"
  Abstract: Stochastic gradient descent (SGD), which dates back to the 1950s, is one of the most popular and effective approaches for performing stochastic optimization. Research on SGD resurged recently in machine learning for optimizing convex loss functions and training nonconvex deep neural networks. The theory assumes that one can easily compute an unbiased gradient estimator, which is usually the case due to the sample average nature of empirical risk minimization. There exist, however, many scenarios (e.g., graphs) where an unbiased estimator may be as expensive to compute as the full gradient because training examples are interconnected. Recently, Chen et al. (2018) proposed using a consistent gradient estimator as an economic alternative. Encouraged by empirical success, we show, in a general setting, that consistent estimators result in the same convergence behavior as do unbiased ones. Our analysis covers strongly convex, convex, and nonconvex objectives. We verify the results with illustrative experiments on synthetic and real-world data. This work opens several new research directions, including the development of more efficient SGD updates with consistent estimators and the design of efficient training algorithms for large-scale graphs.

Connected through hyperedge HE-1 (induced by source paper "differentiable visual computing"):
  with members:
    - "on the convergence of adam and beyond"
    - "elimination of all bad local minima in deep learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "line drawings from 3d models"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "tangent automatic differentiation using source code transformation in python"
    - "approximate program smoothing using mean variance statistics with application to procedural shader bandlimiting"

Connected through hyperedge HE-2 (induced by source paper "sample complexity of estimating the policy gradient for nearly deterministic dynamical systems"):
  with members:
    - "contrasting exploration in parameter and action space a zeroth order optimization perspective"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "residual reinforcement learning for robot control"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "is q learning provably efficient"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-3 (induced by source paper "optimizing nondecomposable data dependent regularizers via lagrangian reparameterization offers significant performance and efficiency gains"):
  with members:
    - "differentiable sorting using optimal transport the sinkhorn cdf and quantile operator"
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "object detection with deep learning a review"
    - "convex constrained sparse additive modeling and its extensions"
    - "ldmnet low dimensional manifold regularized neural networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "automatic differentiation in machine learning a survey"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-4 (induced by source paper "adaptively truncating backpropagation through time to control gradient bias"):
  with members:
    - "approximating real time recurrent learning with random kronecker factors"
    - "unbiasing truncated backpropagation through time"
    - "stable recurrent models"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "quasi recurrent neural networks"
    - "pointer sentinel mixture models"
    - "a recurrent neural network without chaos"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "a stochastic gradient method with biased estimation for faster nonconvex optimization"):
  with members:
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-6 (induced by source paper "optimal design of queuing systems via compositional stochastic programming"):
  with members:
    - "learning and management for internet of things accounting for adaptivity and scalability"
    - "sla oriented resource provisioning for cloud computing challenges architecture and solutions"

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)