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: "distributed tensorflow with mpi"
  Abstract: Machine Learning and Data Mining (MLDM) algorithms are becoming increasingly important in analyzing large volume of data generated by simulations, experiments and mobile devices. With increasing data volume, distributed memory systems (such as tightly connected supercomputers or cloud computing systems) are becoming important in designing in-memory and massively parallel MLDM algorithms. Yet, the majority of open source MLDM software is limited to sequential execution with a few supporting multi-core/many-core execution. #R##N#In this paper, we extend recently proposed Google TensorFlow for execution on large scale clusters using Message Passing Interface (MPI). Our approach requires minimal changes to the TensorFlow runtime -- making the proposed implementation generic and readily usable to increasingly large users of TensorFlow. We evaluate our implementation using an InfiniBand cluster and several well knowndatasets. Our evaluation indicates the efficiency of our proposed implementation.

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 "tensorflow doing hpc"):
  with members:
    - "rpc considered harmful fast distributed deep learning on rdma"
    - "nvidia tensor core programmability performance precision"
    - "designing a micro benchmark suite to evaluate grpc for tensorflow early experiences"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "characterizing deep learning i o workloads in tensorflow"
    - "in datacenter performance analysis of a tensor processing unit"
    - "tensorflow a system for large scale machine learning"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent"):
  with members:
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "gossip training for deep learning"
    - "how to scale distributed deep learning"
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"

Connected through hyperedge HE-4 (induced by source paper "gear training a new way to implement high performance model parallel training"):
  with members:
    - "ampnet asynchronous model parallel training for dynamic neural networks"
    - "stochastic gradient descent on highly parallel architectures"
    - "bigdl a distributed deep learning framework for big data"
    - "gate variants of gated recurrent unit gru neural networks"
    - "revisiting distributed synchronous sgd"
    - "wide deep learning for recommender systems"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "step a distributed multi threading framework towards efficient data analytics"):
  with members:
    - "mllib machine learning in apache spark"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-6 (induced by source paper "distributed learning of cnns on heterogeneous cpu gpu architectures"):
  with members:
    - "giraffe using deep reinforcement learning to play chess"
    - "one weird trick for parallelizing convolutional neural networks"
    - "imagenet large scale visual recognition challenge"
    - "scalable object detection using deep neural networks"
    - "multi gpu training of convnets"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "deep learning in the automotive industry applications and tools"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "an empirical evaluation of deep learning on highway driving"
    - "deep image scaling up image recognition"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-8 (induced by source paper "adaptive neuron apoptosis for accelerating deep learning on large scale systems"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"
    - "auto sizing neural networks with applications to n gram language models"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "deep fried convnets"
    - "caffe convolutional architecture for fast feature embedding"

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)