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: "saga a fast incremental gradient method with support for non strongly convex composite objectives"
  Abstract: In this work we introduce a new optimisation method called SAGA in the spirit of SAG, SDCA, MISO and SVRG, a set of recently proposed incremental gradient algorithms with fast linear convergence rates. SAGA improves on the theory behind SAG and SVRG, with better theoretical convergence rates, and has support for composite objectives where a proximal operator is used on the regulariser. Unlike SDCA, SAGA supports non-strongly convex problems directly, and is adaptive to any inherent strong convexity of the problem. We give experimental results showing the effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "scaffold stochastic controlled averaging for federated learning"):
  with members:
    - "feddane a federated newton type method"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "advances and open problems in federated learning"
    - "how good is sgd with random shuffling"
    - "variance reduced local sgd with lower communication complexity"
    - "federated learning of n gram language models"
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"

Connected through hyperedge HE-2 (induced by source paper "an empirical study of large batch stochastic gradient descent with structured covariance noise"):
  with members:
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "understanding short horizon bias in stochastic meta optimization"
    - "a coordinate free construction of scalable natural gradient"
    - "three factors influencing minima in sgd"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "measuring the effects of data parallelism on neural network training"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "generalization bounds of sgld for non convex learning two theoretical viewpoints"

Connected through hyperedge HE-3 (induced by source paper "federated learning of a mixture of global and local models"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "sgd general analysis and improved rates"
    - "adaptive gradient based meta learning methods"
    - "advances and open problems in federated learning"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "variational federated multi task learning"
    - "variance reduced local sgd with lower communication complexity"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"

Connected through hyperedge HE-4 (induced by source paper "a hybrid stochastic policy gradient algorithm for reinforcement learning"):
  with members:
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "policy optimization with stochastic mirror descent"
    - "regularization matters in policy optimization"
    - "momentum based variance reduction in non convex sgd"
    - "sample efficient policy gradient methods with recursive variance reduction"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "stochastic variance reduced policy gradient"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-5 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization"):
  with members:
    - "variance reduction with sparse gradients"
    - "stochastic newton and cubic newton methods with simple local linear quadratic rates"
    - "sgd general analysis and improved rates"
    - "painless stochastic gradient interpolation line search and convergence rates"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "on the convergence of adam and beyond"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "how to make the gradients small stochastically even faster convex and nonconvex sgd"

Connected through hyperedge HE-6 (induced by source paper "reanalysis of variance reduced temporal difference learning"):
  with members:
    - "finite time error bounds for linear stochastic approximation and td learning"
    - "two time scale off policy td learning non asymptotic analysis over markovian samples"
    - "finite sample analysis for sarsa with linear function approximation"
    - "svrg for policy evaluation with fewer gradient evaluations"
    - "target based temporal difference learning"
    - "characterizing the exact behaviors of temporal difference learning algorithms using markov jump linear system theory"
    - "neural temporal difference learning converges to global optima"
    - "a finite time analysis of temporal difference learning with linear function approximation"

Connected through hyperedge HE-7 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "sgd general analysis and improved rates"
    - "distributed stochastic optimization with gradient tracking over strongly connected networks"
    - "on the performance of exact diffusion over adaptive networks"
    - "an exact quantized decentralized gradient descent algorithm"
    - "on the influence of bias correction on distributed stochastic optimization"
    - "asyspa an exact asynchronous algorithm for convex optimization over digraphs"
    - "stochastic gradient push for distributed deep learning"

Connected through hyperedge HE-8 (induced by source paper "variance reduction with sparse gradients"):
  with members:
    - "on the ineffectiveness of variance reduced optimization for deep learning"
    - "katyusha x practical momentum method for stochastic sum of nonconvex optimization"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "sparsified sgd with memory"
    - "don t decay the learning rate increase the batch size"
    - "sparse communication for distributed gradient descent"
    - "deep residual learning for image recognition"
    - "improved svrg for non strongly convex or sum of non convex 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)