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: "optimized broadcast for deep learning workloads on dense gpu infiniband clusters mpi or nccl"
  Abstract: Dense Multi-GPU systems have recently gained a lot of attention in the HPC arena. Traditionally, MPI runtimes have been primarily designed for clusters with a large number of nodes. However, with the advent of MPI+CUDA applications and CUDA-Aware MPI runtimes like MVAPICH2 and OpenMPI, it has become important to address efficient communication schemes for such dense Multi-GPU nodes. This coupled with new application workloads brought forward by Deep Learning frameworks like Caffe and Microsoft CNTK pose additional design constraints due to very large message communication of GPU buffers during the training phase. In this context, special-purpose libraries like NVIDIA NCCL have been proposed for GPU-based collective communication on dense GPU systems. In this paper, we propose a pipelined chain (ring) design for the MPI_Bcast collective operation along with an enhanced collective tuning framework in MVAPICH2-GDR that enables efficient intra-/inter-node multi-GPU communication. We present an in-depth performance landscape for the proposed MPI_Bcast schemes along with a comparative analysis of NVIDIA NCCL Broadcast and NCCL-based MPI_Bcast. The proposed designs for MVAPICH2-GDR enable up to 14X and 16.6X improvement, compared to NCCL-based solutions, for intra- and inter-node broadcast latency, respectively. In addition, the proposed designs provide up to 7% improvement over NCCL-based solutions for data parallel training of the VGG network on 128 GPUs using Microsoft CNTK.

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 "evaluating modern gpu interconnect pcie nvlink nv sli nvswitch and gpudirect"):
  with members:
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "superneurons dynamic gpu memory management for training deep neural networks"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-3 (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"
    - "beyond data and model parallelism for deep neural networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "priority based parameter propagation for distributed dnn training"):
  with members:
    - "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "tbd benchmarking and analyzing deep neural network training"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "adacomp adaptive residual gradient compression for data parallel distributed training"
    - "sockeye a toolkit for neural machine translation"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-5 (induced by source paper "an empirical evaluation of allgatherv on multi gpu systems"):
  with members:
    - "a unified optimization approach for sparse tensor operations on gpus"
    - "parallel breadth first search on distributed memory systems"

Connected through hyperedge HE-6 (induced by source paper "performance modeling and evaluation of distributed deep learning frameworks on gpus"):
  with members:
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "stochastic nonconvex optimization with large minibatches"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "comparative study of deep learning software frameworks"
    - "understanding deep learning requires rethinking generalization"
    - "benchmarking state of the art deep learning software tools"
    - "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.DC(Distributed, Parallel, and Cluster Computing)