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: "randomized block coordinate descent for online and stochastic optimization"
  Abstract: Two types of low cost-per-iteration gradient descent methods have been extensively studied in parallel. One is online or stochastic gradient descent (OGD/SGD), and the other is randomzied coordinate descent (RBCD). In this paper, we combine the two types of methods together and propose online randomized block coordinate descent (ORBCD). At each iteration, ORBCD only computes the partial gradient of one block coordinate of one mini-batch samples. ORBCD is well suited for the composite minimization problem where one function is the average of the losses of a large number of samples and the other is a simple regularizer defined on high dimensional variables. We show that the iteration complexity of ORBCD has the same order as OGD or SGD. For strongly convex functions, by reducing the variance of stochastic gradients, we show that ORBCD can converge at a geometric rate in expectation, matching the convergence rate of SGD with variance reduction and RBCD.

Connected through hyperedge HE-1 (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"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "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-2 (induced by source paper "block stochastic gradient descent for large scale tomographic reconstruction in a parallel network"):
  with members:
    - "online learning rate adaptation with hypergradient descent"
    - "an overview of gradient descent optimization algorithms"
    - "fast distributed asynchronous sgd with variance reduction"

Connected through hyperedge HE-3 (induced by source paper "a forest mixture bound for block free parallel inference"):
  with members:
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-4 (induced by source paper "accelerated randomized coordinate descent algorithms for stochastic optimization and online learning"):
  with members:
    - "federated optimization distributed machine learning for on device intelligence"

Connected through hyperedge HE-5 (induced by source paper "stochastic distributed and federated optimization for machine learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "randomized distributed mean estimation accuracy vs communication"
    - "sdca without duality regularization and individual convexity"
    - "importance sampling for minibatches"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "practical secure aggregation for federated learning on user held data"

Connected through hyperedge HE-6 (induced by source paper "block cyclic stochastic coordinate descent for deep neural networks"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "squeeze and excitation networks"
    - "dual path networks"
    - "aggregated residual transformations for deep neural networks"
    - "identity mappings in deep residual networks"
    - "big batch sgd automated inference using adaptive batch sizes"

Connected through hyperedge HE-7 (induced by source paper "federated optimization distributed machine learning for on device intelligence"):
  with members:
    - "sdca without duality regularization and individual convexity"
    - "exploiting the structure stochastic gradient methods using raw clusters"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "stop wasting my gradients practical svrg"
    - "distributed optimization with arbitrary local solvers"
    - "distributed mini batch sdca"

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)