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: "mini batch semi stochastic gradient descent in the proximal setting"
  Abstract: We propose mS2GD: a method incorporating a mini-batching scheme for improving the theoretical complexity and practical performance of semi-stochastic gradient descent (S2GD). We consider the problem of minimizing a strongly convex function represented as the sum of an average of a large number of smooth convex functions, and a simple nonsmooth convex regularizer. Our method first performs a deterministic step (computation of the gradient of the objective function at the starting point), followed by a large number of stochastic steps. The process is repeated a few times with the last iterate becoming the new starting point. The novelty of our method is in introduction of mini-batching into the computation of stochastic steps. In each step, instead of choosing a single function, we sample   $b$   functions, compute their gradients, and compute the direction based on this. We analyze the complexity of the method and show that it benefits from two speedup effects. First, we prove that as long as   $b$   is below a certain threshold, we can reach any predefined accuracy with less overall work than without mini-batching. Second, our mini-batching scheme admits a simple parallel implementation, and hence is suitable for further acceleration by parallelization.

Connected through hyperedge HE-1 (induced by source paper "dynamic mini batch sgd for elastic distributed training learning in the limbo of resources"):
  with members:
    - "bag of freebies for training object detection neural networks"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "bag of tricks for image classification with convolutional neural networks"
    - "an empirical model of large batch training"
    - "low latency video semantic segmentation"
    - "three factors influencing minima in sgd"
    - "slowfast networks for video recognition"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-2 (induced by source paper "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "direct acceleration of saga using sampled negative momentum"
    - "sdca without duality regularization and individual convexity"
    - "sdna stochastic dual newton ascent for empirical risk minimization"
    - "variance reduced stochastic gradient descent with neighbors"
    - "finito a faster permutable incremental gradient method for big data problems"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "mini batch primal and dual methods for svms"

Connected through hyperedge HE-3 (induced by source paper "accelerating mini batch sarah by step size rules"):
  with members:
    - "stochastic cubic regularization for fast nonconvex optimization"
    - "online learning rate adaptation with hypergradient descent"
    - "importance sampling for minibatches"
    - "probabilistic line searches for stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "better mini batch algorithms via accelerated gradient methods"

Connected through hyperedge HE-4 (induced by source paper "saags biased stochastic variance reduction methods for large scale learning"):
  with members:
    - "stochastic trust region inexact newton method for large scale machine learning"
    - "vr sgd a simple stochastic variance reduction method for machine learning"
    - "faster learning by reduction of data access time"
    - "randomized block coordinate descent for online and stochastic optimization"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "saga with arbitrary sampling"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "importance sampling for minibatches"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-6 (induced by source paper "sparse regression and adaptive feature generation for the discovery of dynamical systems"):
  with members:
    - "safe feature elimination for the lasso and sparse supervised learning problems"

Connected through hyperedge HE-7 (induced by source paper "asvrg accelerated proximal svrg"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "vr sgd a simple stochastic variance reduction method for machine learning"
    - "accelerated variance reduced stochastic admm"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "variance reduced and projection free stochastic optimization"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "an overview of gradient descent optimization algorithms"

Connected through hyperedge HE-8 (induced by source paper "stochastic variance reduced policy gradient"):
  with members:
    - "policy gradient methods for reinforcement learning with function approximation and action dependent baselines"
    - "stochastic variance reduction for policy gradient estimation"
    - "stochastic variance reduction methods for policy evaluation"
    - "stochastic variance reduction methods for saddle point problems"
    - "benchmarking deep reinforcement learning for continuous control"
    - "stop wasting my gradients practical svrg"
    - "adam a method for stochastic optimization"
    - "finito a faster permutable incremental gradient method for big data problems"

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)