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: "understanding top k sparsification in distributed deep learning"
  Abstract: Distributed stochastic gradient descent (SGD) algorithms are widely deployed in training large-scale deep learning models, while the communication overhead among workers becomes the new system bottleneck. Recently proposed gradient sparsification techniques, especially Top-$k$ sparsification with error compensation (TopK-SGD), can significantly reduce the communication traffic without an obvious impact on the model accuracy. Some theoretical studies have been carried out to analyze the convergence property of TopK-SGD. However, existing studies do not dive into the details of Top-$k$ operator in gradient sparsification and use relaxed bounds (e.g., exact bound of Random-$k$) for analysis; hence the derived results cannot well describe the real convergence performance of TopK-SGD. To this end, we first study the gradient distributions of TopK-SGD during the training process through extensive experiments. We then theoretically derive a tighter bound for the Top-$k$ operator. Finally, we exploit the property of gradient distribution to propose an approximate top-$k$ selection algorithm, which is computing-efficient for GPUs, to improve the scaling efficiency of TopK-SGD by significantly reducing the computing overhead. Codes are available at: \url{this https URL}.

Connected through hyperedge HE-1 (induced by source paper "communication efficient decentralized learning with sparsification and adaptive peer selection"):
  with members:
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "communication compression for decentralized training"
    - "federated learning with non iid data"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "layer wise adaptive gradient sparsification for distributed deep learning with convergence guarantees"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "on the discrepancy between the theoretical analysis and practical implementations of compressed communication for distributed deep learning"
    - "communication efficient distributed sgd with sketching"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "mg wfbp efficient data communication for distributed synchronous sgd algorithms"
    - "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)