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: "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
  Abstract: Distributed synchronous stochastic gradient descent (S-SGD) has been widely used in training large-scale deep neural networks (DNNs), but it typically requires very high communication bandwidth between computational workers (e.g., GPUs) to exchange gradients iteratively. Recently, Top-$k$ sparsification techniques have been proposed to reduce the volume of data to be exchanged among workers. Top-$k$ sparsification can zero-out a significant portion of gradients without impacting the model convergence. However, the sparse gradients should be transferred with their irregular indices, which makes the sparse gradients aggregation difficult. Current methods that use AllGather to accumulate the sparse gradients have a communication complexity of $O(kP)$, where $P$ is the number of workers, which is inefficient on low bandwidth networks with a large number of workers. We observe that not all top-$k$ gradients from $P$ workers are needed for the model update, and therefore we propose a novel global Top-$k$ (gTop-$k$) sparsification mechanism to address the problem. Specifically, we choose global top-$k$ largest absolute values of gradients from $P$ workers, instead of accumulating all local top-$k$ gradients to update the model in each iteration. The gradient aggregation method based on gTop-$k$ sparsification reduces the communication complexity from $O(kP)$ to $O(k\log P)$. Through extensive experiments on different DNNs, we verify that gTop-$k$ S-SGD has nearly consistent convergence performance with S-SGD, and it has only slight degradations on generalization performance. In terms of scaling efficiency, we evaluate gTop-$k$ on a cluster with 32 GPU machines which are interconnected with 1 Gbps Ethernet. The experimental results show that our method achieves $2.7-12\times$ higher scaling efficiency than S-SGD and $1.1-1.7\times$ improvement than the existing Top-$k$ S-SGD.

Connected through hyperedge HE-1 (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"
    - "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"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "mdldroid a chainsgd reduce approach to mobile deep learning for personal mobile sensing"):
  with members:
    - "deep learning on mobile devices a review"
    - "layered sgd a decentralized and synchronous sgd algorithm for scalable deep neural network training"
    - "towards federated learning at scale system design"
    - "multi modality sensor data classification with selective attention"
    - "analyzing federated learning through an adversarial lens"
    - "deep learning towards mobile applications"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"

Connected through hyperedge HE-3 (induced by source paper "communication efficient decentralized learning with sparsification and adaptive peer selection"):
  with members:
    - "understanding top k sparsification in distributed deep learning"
    - "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-4 (induced by source paper "communication contention aware scheduling of multiple deep learning training jobs"):
  with members:
    - "analysis of large scale multi tenant gpu clusters for dnn training workloads"
    - "low latency job scheduling with preemption for the development of deep learning"
    - "tony an orchestrator for distributed machine learning jobs"
    - "online job scheduling in distributed machine learning clusters"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "scaling deep learning on gpu and knights landing clusters"

Connected through hyperedge HE-5 (induced by source paper "model pruning enables efficient federated learning on edge devices"):
  with members:
    - "advances and open problems in federated learning"
    - "performance analysis and characterization of training deep learning models on nvidia tx2"
    - "prunetrain gradual structured pruning from scratch for faster neural network training"
    - "distilling on device intelligence at the network edge"
    - "federated learning challenges methods and future directions"
    - "prunetrain fast neural network training by dynamic sparse model reconfiguration"
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "understanding top k sparsification in distributed deep learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "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"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "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-8 (induced by source paper "mg wfbp merging gradients wisely for efficient communication in distributed deep learning"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "mg wfbp efficient data communication for distributed synchronous sgd algorithms"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "scaling deep learning on gpu and knights landing clusters"
    - "mixed precision training"
    - "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.DC(Distributed, Parallel, and Cluster Computing)