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: "toward understanding the impact of staleness in distributed machine learning"
  Abstract: Many distributed machine learning (ML) systems adopt the non-synchronous execution in order to alleviate the network communication bottleneck, resulting in stale parameters that do not reflect the latest updates. Despite much development in large-scale ML, the effects of staleness on learning are inconclusive as it is challenging to directly monitor or control staleness in complex distributed environments. In this work, we study the convergence behaviors of a wide array of ML models and algorithms under delayed updates. Our extensive experiments reveal the rich diversity of the effects of staleness on the convergence of ML algorithms and offer insights into seemingly contradictory reports in the literature. The empirical findings also inspire a new convergence analysis of stochastic gradient descent in non-convex optimization under staleness, matching the best-known convergence rate of O(1/\sqrt{T}).

Connected through hyperedge HE-1 (induced by source paper "elastic consistency a general consistency model for distributed stochastic gradient descent"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "communication compression for decentralized training"
    - "fault tolerance in iterative convergent machine learning"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "sparsified sgd with memory"
    - "gradient sparsification for communication efficient distributed optimization"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-2 (induced by source paper "age based scheduling policy for federated learning in mobile edge networks"):
  with members:
    - "federated learning over wireless fading channels"
    - "scheduling policies for federated learning in wireless networks"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "the roadmap to 6g ai empowered wireless networks"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "a joint learning and communications framework for federated learning over wireless networks"
    - "high dimensional stochastic gradient quantization for communication efficient edge learning"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-3 (induced by source paper "taming momentum in a distributed asynchronous environment"):
  with members:
    - "image classification at supercomputer scale"
    - "signsgd compressed optimisation for non convex problems"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "visualizing the loss landscape of neural nets"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "wide residual networks"

Connected through hyperedge HE-4 (induced by source paper "sparsification as a remedy for staleness in distributed asynchronous sgd"):
  with members:
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "gradient sparsification for communication efficient distributed optimization"
    - "sparse communication for distributed gradient descent"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "opening the black box of deep neural networks via information"
    - "revisiting distributed synchronous sgd"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

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)