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: "communication efficient distributed blockwise momentum sgd with error feedback"
  Abstract: Communication overhead is a major bottleneck hampering the scalability of distributed machine learning systems. Recently, there has been a surge of interest in using gradient compression to improve the communication efficiency of distributed neural network training. Using 1-bit quantization, signSGD with majority vote achieves a 32x reduction on communication cost. However, its convergence is based on unrealistic assumptions and can diverge in practice. In this paper, we propose a general distributed compressed SGD with Nesterov's momentum. We consider two-way compression, which compresses the gradients both to and from workers. Convergence analysis on nonconvex problems for general gradient compressors is provided. By partitioning the gradient into blocks, a blockwise compressor is introduced such that each gradient block is compressed and transmitted in 1-bit format with a scaling factor, leading to a nearly 32x reduction on communication. Experimental results show that the proposed method converges as fast as full-precision distributed momentum SGD and achieves the same testing accuracy. In particular, on distributed ResNet training with 7 workers on the ImageNet, the proposed algorithm achieves the same testing accuracy as momentum SGD using full-precision gradients, but with $46\%$ less wall clock time.

Connected through hyperedge HE-1 (induced by source paper "stochastic sign sgd for federated learning with theoretical guarantees"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "slsgd secure and efficient distributed on device machine learning"
    - "distributed training with heterogeneous data bridging median and mean based algorithms"
    - "byzantine stochastic gradient descent"
    - "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
    - "communication compression for decentralized training"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"

Connected through hyperedge HE-2 (induced by source paper "local adaalter communication efficient stochastic gradient descent with adaptive learning rates"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "reducing bert pre training time from 3 days to 76 minutes"
    - "large batch optimization for deep learning training bert in 76 minutes"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "a sufficient condition for convergences of adam and rmsprop"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"
    - "sparsified sgd with memory"

Connected through hyperedge HE-3 (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"
    - "understanding top k sparsification in distributed deep learning"
    - "communication efficient distributed sgd with sketching"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "understanding top k sparsification in distributed deep learning"):
  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"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"
    - "sparsified sgd with memory"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"

Connected through hyperedge HE-5 (induced by source paper "high dimensional stochastic gradient quantization for communication efficient edge learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "signsgd compressed optimisation for non convex problems"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "communication efficient learning of deep networks from decentralized data"
    - "compressed channel feedback for correlated massive mimo systems"
    - "adam a method for stochastic optimization"

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)