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: "terngrad ternary gradients to reduce communication in distributed deep learning"
  Abstract: High network communication cost for synchronizing gradients and parameters is the well-known bottleneck of distributed training. In this work, we propose TernGrad that uses ternary gradients to accelerate distributed deep learning in data parallelism. Our approach requires only three numerical levels {-1,0,1}, which can aggressively reduce the communication time. We mathematically prove the convergence of TernGrad under the assumption of a bound on gradients. Guided by the bound, we propose layer-wise ternarizing and gradient clipping to improve its convergence. Our experiments show that applying TernGrad on AlexNet does not incur any accuracy loss and can even improve accuracy. The accuracy loss of GoogLeNet induced by TernGrad is less than 2% on average. Finally, a performance model is proposed to study the scalability of TernGrad. Experiments show significant speed gains for various deep neural networks. Our source code is available.

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 "trends and advancements in deep neural network communication"):
  with members:
    - "deepcabac a universal compression algorithm for deep neural networks"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "advances and open problems in federated learning"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "improving federated learning personalization via model agnostic meta learning"
    - "hierarchical federated learning across heterogeneous cellular networks"

Connected through hyperedge HE-3 (induced by source paper "disentangling adaptive gradient methods from learning rates"):
  with members:
    - "the step decay schedule a near optimal geometrically decaying learning rate procedure for least squares"
    - "extreme tensoring for low memory preconditioning"
    - "on empirical comparisons of optimizers for deep learning"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "stochastic gradient methods with layer wise adaptive moments for training of deep networks"
    - "escaping saddle points with adaptive gradient methods"
    - "an exponential learning rate schedule for deep learning"
    - "deepobs a deep learning optimizer benchmark suite"

Connected through hyperedge HE-4 (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-5 (induced by source paper "ternary compression for communication efficient federated learning"):
  with members:
    - "a survey on federated learning systems vision hype and reality for data privacy and protection"
    - "towards federated learning at scale system design"
    - "robust and communication efficient federated learning from non iid data"
    - "federated machine learning concept and applications"
    - "federated learning in mobile edge networks a comprehensive survey"
    - "model compression via distillation and quantization"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "multi objective evolutionary federated learning"

Connected through hyperedge HE-6 (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-7 (induced by source paper "a federated learning framework for privacy preserving and parallel training"):
  with members:
    - "privacy preserving deep learning via weight transmission"
    - "towards fair and decentralized privacy preserving deep learning with blockchain"
    - "federated machine learning concept and applications"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "beyond data and model parallelism for deep neural networks"
    - "optimize deep convolutional neural network with ternarized weights and high accuracy"
    - "privacy preserving distributed deep learning for clinical data"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-8 (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"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "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"

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)