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: "sgd general analysis and improved rates"
  Abstract: We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of variants of SGD, each of which is associated with a specific probability law governing the data selection rule used to form mini-batches. This is the first time such an analysis is performed, and most of our variants of SGD were never explicitly considered in the literature before. Our analysis relies on the recently introduced notion of expected smoothness and does not rely on a uniform bound on the variance of the stochastic gradients. By specializing our theorem to different mini-batching strategies, such as sampling with replacement and independent sampling, we derive exact expressions for the stepsize as a function of the mini-batch size. With this we can also determine the mini-batch size that optimizes the total complexity, and show explicitly that as the variance of the stochastic gradient evaluated at the minimum grows, so does the optimal mini-batch size. For zero variance, the optimal mini-batch size is one. Moreover, we prove insightful stepsize-switching rules which describe when one should switch from a constant to a decreasing stepsize regime.

Connected through hyperedge HE-1 (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"
    - "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"
    - "semi cyclic stochastic gradient descent"

Connected through hyperedge HE-2 (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"
    - "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"
    - "stochastic cubic regularization for fast nonconvex optimization"

Connected through hyperedge HE-3 (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"
    - "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"
    - "variance reduced stochastic learning by networked agents under random reshuffling"

Connected through hyperedge HE-4 (induced by source paper "on the convergence of nesterov s accelerated gradient method in stochastic settings"):
  with members:
    - "accelerating sgd with momentum for over parameterized learning"
    - "understanding the role of momentum in stochastic gradient methods"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "quasi hyperbolic momentum and adam for deep learning"
    - "negative momentum for improved game dynamics"
    - "on the curved geometry of accelerated optimization"
    - "linear coupling an ultimate unification of gradient and mirror descent"

Connected through hyperedge HE-5 (induced by source paper "the error feedback framework better rates for sgd with delayed gradients and compressed communication"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "unified optimal analysis of the stochastic gradient method"
    - "the complexity of making the gradient small in stochastic convex optimization"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"

Connected through hyperedge HE-6 (induced by source paper "an exponential learning rate schedule for deep learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "fix your classifier the marginal value of training the last weight layer"
    - "group normalization"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "three mechanisms of weight decay regularization"
    - "understanding batch normalization"
    - "scaling sgd batch size to 32k for imagenet training"
    - "riemannian approach to batch normalization"

Connected through hyperedge HE-7 (induced by source paper "a double residual compression algorithm for efficient distributed learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "distributed learning with compressed gradient differences"
    - "communication compression for decentralized training"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "imagenet training in minutes"
    - "gradient sparsification for communication efficient distributed optimization"

Connected through hyperedge HE-8 (induced by source paper "distributed fixed point methods with compressed iterates"):
  with members:
    - "distributed learning with compressed gradient differences"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "natural compression for distributed deep learning"
    - "fedpaq a communication efficient federated learning method with periodic averaging and quantization"
    - "gradient descent with compressed iterates"
    - "federated learning strategies for improving communication efficiency"
    - "quantized decentralized consensus optimization"
    - "sparsified sgd with memory"

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)