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: "on the ineffectiveness of variance reduced optimization for deep learning"
  Abstract: The application of stochastic variance reduction to optimization has shown remarkable recent theoretical and practical success. The applicability of these techniques to the hard non-convex optimization problems encountered during training of modern deep neural networks is an open problem. We show that naive application of the SVRG technique and related approaches fail, and explore why.

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 "variance reduction with sparse gradients"):
  with members:
    - "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"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "distributed learning of deep neural networks using independent subnet training"):
  with members:
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "mlsys the new frontier of machine learning systems"
    - "large batch training for lstm and beyond"
    - "communication efficient distributed sgd with sketching"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "inefficiency of k fac for large batch size training"
    - "survey of dropout methods for deep neural networks"
    - "understanding the disharmony between dropout and batch normalization by variance shift"

Connected through hyperedge HE-4 (induced by source paper "painless stochastic gradient interpolation line search and convergence rates"):
  with members:
    - "training neural networks for and by interpolation"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "a variational inequality perspective on generative adversarial nets"
    - "l4 practical loss based stepsize adaptation for deep learning"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "an alternative view when does sgd escape local minima"
    - "online learning rate adaptation with hypergradient descent"

Connected through hyperedge HE-5 (induced by source paper "lookahead optimizer k steps forward 1 step back"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "on first order meta learning algorithms"
    - "understanding short horizon bias in stochastic meta optimization"
    - "reptile a scalable metalearning algorithm"
    - "aggregated momentum stability through passive damping"
    - "averaging weights leads to wider optima and better generalization"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "adafactor adaptive learning rates with sublinear memory cost"

Connected through hyperedge HE-6 (induced by source paper "on the convergence of sarah and beyond"):
  with members:
    - "almost tune free variance reduction"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "stochastic nested variance reduction for nonconvex optimization"
    - "how to escape saddle points efficiently"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-7 (induced by source paper "momentum based variance reduction in non convex sgd"):
  with members:
    - "on the convergence of adam and beyond"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "don t use large mini batches use local sgd"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "advances and open problems in federated learning"
    - "communication trade offs for synchronized distributed sgd with large step size"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "federated learning strategies for improving communication efficiency"
    - "an empirical model of large batch training"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"
    - "the convergence of stochastic gradient descent in asynchronous shared 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)