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: "don t use large mini batches use local sgd"
  Abstract: Mini-batch stochastic gradient methods (SGD) are state of the art for distributed training of deep neural networks. Drastic increases in the mini-batch sizes have lead to key efficiency and scalability gains in recent years. However, progress faces a major roadblock, as models trained with large batches often do not generalize well. Local SGD can offer the same communication vs. computation pattern as mini-batch SGD---thus is as efficient as mini-batch SGD from a systems perspective---but instead of performing a single large-batch update in each round, it performs several local parameter updates sequentially. We extensively study the communication efficiency vs. performance trade-offs associated with local SGD and provide a new variant, called \emph{post-local SGD}. We show that it significantly improves the generalization performance compared to large-batch training and converges to flatter minima.

Connected through hyperedge HE-1 (induced by source paper "faster on device training using new federated momentum algorithm"):
  with members:
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "secureboost a lossless federated learning framework"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"

Connected through hyperedge HE-2 (induced by source paper "scalable and practical natural gradient for large scale deep learning"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "limitations of the empirical fisher approximation"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "information matrices and generalization"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "image classification at supercomputer scale"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-3 (induced by source paper "stochastic weight averaging in parallel large batch training that generalizes well"):
  with members:
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "swalp stochastic weight averaging in low precision training"
    - "federated learning challenges methods and future directions"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "an empirical model of large batch training"
    - "averaging weights leads to wider optima and better generalization"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"

Connected through hyperedge HE-4 (induced by source paper "personalized federated learning a meta learning approach"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "adaptive gradient based meta learning methods"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "one shot federated learning"
    - "improving federated learning personalization via model agnostic meta learning"
    - "on the convergence of local descent methods in federated learning"
    - "alpha maml adaptive model agnostic meta learning"
    - "fedpaq a communication efficient federated learning method with periodic averaging and quantization"

Connected through hyperedge HE-5 (induced by source paper "is local sgd better than minibatch sgd"):
  with members:
    - "the error feedback framework better rates for sgd with delayed gradients and compressed communication"
    - "unified optimal analysis of the stochastic gradient method"
    - "local sgd with periodic averaging tighter analysis and adaptive synchronization"
    - "on the randomized complexity of minimizing a convex quadratic function"
    - "on the convergence properties of a k step averaging stochastic gradient descent algorithm for nonconvex optimization"
    - "cooperative sgd a unified framework for the design and analysis of communication efficient sgd algorithms"
    - "memory and communication efficient distributed stochastic optimization with minibatch prox"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-6 (induced by source paper "exploiting unlabeled data in smart cities using federated learning"):
  with members:
    - "federated learning for edge networks resource optimization and incentive mechanism"
    - "federated learning in mobile edge networks a comprehensive survey"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "federated multi task learning"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "communication efficient learning of deep networks from decentralized data"
    - "communication efficient distributed optimization using an approximate newton type method"

Connected through hyperedge HE-7 (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"
    - "on the ineffectiveness of variance reduced optimization for deep learning"

Connected through hyperedge HE-8 (induced by source paper "machine learning at the wireless edge distributed stochastic gradient descent over the air"):
  with members:
    - "computation scheduling for distributed machine learning with straggling workers"
    - "hierarchical coded computation"
    - "wireless network intelligence at the edge"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "sparcml high performance sparse communication for machine learning"
    - "broadband analog aggregation for low latency federated edge learning extended version"
    - "federated learning via over the air computation"

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)