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: "beyond data and model parallelism for deep neural networks"
  Abstract: The computational requirements for training deep neural networks (DNNs) have grown to the point that it is now standard practice to parallelize training. Existing deep learning systems commonly use data or model parallelism, but unfortunately, these strategies often result in suboptimal parallelization performance. #R##N#In this paper, we define a more comprehensive search space of parallelization strategies for DNNs called SOAP, which includes strategies to parallelize a DNN in the Sample, Operation, Attribute, and Parameter dimensions. We also propose FlexFlow, a deep learning framework that uses guided randomized search of the SOAP space to find a fast parallelization strategy for a specific parallel machine. To accelerate this search, FlexFlow introduces a novel execution simulator that can accurately predict a parallelization strategy's performance and is three orders of magnitude faster than prior approaches that have to execute each strategy. We evaluate FlexFlow with six real-world DNN benchmarks on two GPU clusters and show that FlexFlow can increase training throughput by up to 3.8x over state-of-the-art approaches, even when including its search time, and also improves scalability.

Connected through hyperedge HE-1 (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"
    - "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"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"

Connected through hyperedge HE-2 (induced by source paper "simulating performance of ml systems with offline profiling"):
  with members:
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "device placement optimization with reinforcement learning"
    - "densely connected convolutional networks"
    - "tensorflow a system for large scale machine learning"
    - "rethinking the inception architecture for computer vision"
    - "one weird trick for parallelizing convolutional neural networks"
    - "one billion word benchmark for measuring progress in statistical language modeling"

Connected through hyperedge HE-3 (induced by source paper "megatron lm training multi billion parameter language models using model parallelism"):
  with members:
    - "generalization through memorization nearest neighbor language models"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "dynamic evaluation of transformer language models"
    - "spanbert improving pre training by representing and predicting spans"
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "reducing bert pre training time from 3 days to 76 minutes"

Connected through hyperedge HE-4 (induced by source paper "checkmate breaking the memory wall with optimal tensor rematerialization"):
  with members:
    - "the ooo vliw jit compiler for gpu inference"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "roberta a robustly optimized bert pretraining approach"
    - "pytorch an imperative style high performance deep learning library"
    - "group normalization"
    - "an empirical model of large batch training"
    - "combinatorial register allocation and instruction scheduling"

Connected through hyperedge HE-5 (induced by source paper "accelerated training for cnn distributed deep learning through automatic resource aware layer placement"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "pipedream fast and efficient pipeline parallel dnn training"
    - "exploring hidden dimensions in parallelizing convolutional neural networks"
    - "gradient sparsification for communication efficient distributed optimization"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-6 (induced by source paper "performance analysis and characterization of training deep learning models on mobile devices"):
  with members:
    - "towards federated learning at scale system design"
    - "agnostic federated learning"
    - "applied federated learning improving google keyboard query suggestions"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "latency and throughput characterization of convolutional neural networks for mobile computer vision"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-7 (induced by source paper "performance analysis and characterization of training deep learning models on nvidia tx2"):
  with members:
    - "towards federated learning at scale system design"
    - "agnostic federated learning"
    - "applied federated learning improving google keyboard query suggestions"
    - "deepcache principled cache for mobile deep vision"
    - "federated learning strategies for improving communication efficiency"
    - "runtime concurrency control and operation scheduling for high performance neural network training"
    - "federated optimization for heterogeneous networks"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-8 (induced by source paper "heterogeneity aware asynchronous decentralized training"):
  with members:
    - "supporting very large models using automatic dataflow graph partitioning"
    - "hop heterogeneity aware decentralized training"
    - "yet another accelerated sgd resnet 50 training on imagenet in 74 7 seconds"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "communication compression for decentralized training"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update 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.DC(Distributed, Parallel, and Cluster Computing)