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: "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
  Abstract: It is important to scale out deep neural network (DNN) training for reducing model training time. The high communication overhead is one of the major performance bottlenecks for distributed DNN training across multiple GPUs. Our investigations have shown that popular open-source DNN systems could only achieve 2.5 speedup ratio on 64 GPUs connected by 56 Gbps network. To address this problem, we propose a communication backend named GradientFlow for distributed DNN training, and employ a set of network optimization techniques. First, we integrate ring-based allreduce, mixed-precision training, and computation/communication overlap into GradientFlow. Second, we propose lazy allreduce to improve network throughput by fusing multiple communication operations into a single one, and design coarse-grained sparse communication to reduce network traffic by only transmitting important gradient chunks. When training ImageNet/AlexNet on 512 GPUs, our approach achieves 410.2 speedup ratio and completes 95-epoch training in 1.5 minutes, which outperforms existing approaches.

Connected through hyperedge HE-1 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts"):
  with members:
    - "scaling laws for neural language models"
    - "zero memory optimization towards training a trillion parameter models"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "natural compression for distributed deep learning"
    - "defending against neural fake news"
    - "large scale learning of general visual representations for transfer"
    - "pipemare asynchronous pipeline parallel dnn training"
    - "the hsic bottleneck deep learning without back propagation"

Connected through hyperedge HE-2 (induced by source paper "mlperf training benchmark"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "scalable realistic recommendation datasets through fractal expansions"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "scalable methods for 8 bit training of neural networks"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "nvidia tensor core programmability performance precision"
    - "image classification at supercomputer scale"

Connected through hyperedge HE-3 (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"
    - "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"
    - "beyond data and model parallelism for deep neural networks"
    - "adaptive communication strategies to achieve the best error runtime trade off in local update sgd"

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"
    - "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"

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)