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: "gradient sparsification for communication efficient distributed optimization"
  Abstract: Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the communication overhead for exchanging information such as stochastic gradients among different workers. In this paper, to reduce the communication cost we propose a convex optimization formulation to minimize the coding length of stochastic gradients. To solve the optimal sparsification efficiently, several simple and fast algorithms are proposed for approximate solution, with theoretical guaranteed for sparseness. Experiments on $\ell_2$ regularized logistic regression, support vector machines, and convolutional neural networks validate our sparsification approaches.

Connected through hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "priority based parameter propagation for distributed dnn training"
    - "large batch training for lstm and beyond"
    - "scalable deep learning on distributed infrastructures challenges techniques and tools"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "redsync reducing synchronization bandwidth for distributed deep learning training system"

Connected through hyperedge HE-2 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach"):
  with members:
    - "model pruning enables efficient federated learning on edge devices"
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "advances and open problems in federated learning"
    - "introduction to online convex optimization"
    - "federated learning challenges methods and future directions"
    - "layer wise adaptive gradient sparsification for distributed deep learning with convergence guarantees"
    - "federated machine learning concept and applications"
    - "wireless network intelligence at the edge"

Connected through hyperedge HE-3 (induced by source paper "optimal gradient quantization condition for communication efficient distributed training"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "federated learning strategies for improving communication efficiency"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "loss aware weight quantization of deep networks"
    - "sparsified sgd with memory"
    - "signsgd compressed optimisation for non convex problems"
    - "error compensated quantized sgd and its applications to large scale distributed optimization"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-4 (induced by source paper "communication efficient federated learning over multiple access channels"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "collaborative machine learning at the wireless edge with blind transmitters"
    - "energy aware analog aggregation for federated learning with redundant data"
    - "hierarchical federated learning across heterogeneous cellular networks"
    - "federated learning over wireless fading channels"
    - "on analog gradient descent learning over multiple access fading channels"
    - "a joint learning and communications framework for federated learning over wireless networks"

Connected through hyperedge HE-5 (induced by source paper "uncertainty principle for communication compression in distributed and federated learning and the search for an optimal compressor"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "natural compression for distributed deep learning"
    - "federated learning challenges methods and future directions"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"

Connected through hyperedge HE-6 (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"
    - "toward understanding the impact of staleness in distributed machine learning"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-7 (induced by source paper "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach"):
  with members:
    - "robust and communication efficient collaborative learning"
    - "dp lssgd a stochastic optimization method to lift the utility in privacy preserving erm"
    - "an exact quantized decentralized gradient descent algorithm"
    - "communication compression for decentralized training"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "leasgd an efficient and privacy preserving decentralized algorithm for distributed learning"
    - "efficient private erm for smooth objectives"
    - "renyi differential privacy"

Connected through hyperedge HE-8 (induced by source paper "basgd buffered asynchronous sgd for byzantine learning"):
  with members:
    - "advances and open problems in federated learning"
    - "zeno robust asynchronous sgd with arbitrary number of byzantine workers"
    - "byzantine stochastic gradient descent"
    - "federated learning strategies for improving communication efficiency"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "zeno distributed stochastic gradient descent with suspicion based fault tolerance"
    - "adding vs averaging in distributed primal dual optimization"
    - "deep residual learning for image recognition"

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)