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: "accelerating minibatch stochastic gradient descent using typicality sampling"
  Abstract: Machine learning, especially deep neural networks, has been rapidly developed in fields including computer vision, speech recognition and reinforcement learning. Although Mini-batch SGD is one of the most popular stochastic optimization methods in training deep networks, it shows a slow convergence rate due to the large noise in gradient approximation. In this paper, we attempt to remedy this problem by building more efficient batch selection method based on typicality sampling, which reduces the error of gradient estimation in conventional Minibatch SGD. We analyze the convergence rate of the resulting typical batch SGD algorithm and compare convergence properties between Minibatch SGD and the algorithm. Experimental results demonstrate that our batch selection scheme works well and more complex Minibatch SGD variants can benefit from the proposed batch selection strategy.

Connected through hyperedge HE-1 (induced by source paper "learning to auto weight entirely data driven and highly efficient weighting framework"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "autoaugment learning augmentation policies from data"
    - "learning to reweight examples for robust deep learning"
    - "automated curriculum learning for neural networks"
    - "exploring generalization in deep learning"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "parallel stochastic optimization framework for large scale non convex stochastic problems"):
  with members:
    - "an energy efficient controller for wirelessly powered communication networks"
    - "efficient fair and qos aware policies for wirelessly powered communication networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on breast cancer detection an application of machine learning algorithms on the wisconsin diagnostic dataset"
    - "energy entropy competition and the effectiveness of stochastic gradient descent in machine learning"
    - "optimal hierarchical radio resource management for hetnets with flexible backhaul"
    - "adam a method for stochastic optimization"
    - "flexible parallel algorithms for big data optimization"

Connected through hyperedge HE-3 (induced by source paper "a stochastic quasi newton method with nesterov s accelerated gradient"):
  with members:
    - "implementation of stochastic quasi newton s method in pytorch"
    - "implementation of a modified nesterov s accelerated quasi newton method on tensorflow"
    - "a variance reduced stochastic newton method"
    - "adam a method for stochastic optimization"
    - "res regularized stochastic bfgs algorithm"

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)