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: "improving strong scaling of cnn training by exploiting finer grained parallelism"
  Abstract: Scaling CNN training is necessary to keep up with growing datasets and reduce training time. We also see an emerging need to handle datasets with very large samples, where memory requirements for training are large. Existing training frameworks use a data-parallel approach that partitions samples within a mini-batch, but limits to scaling the mini-batch size and memory consumption makes this untenable for large samples. We describe and implement new approaches to convolution, which parallelize using spatial decomposition or a combination of sample and spatial decomposition. This introduces many performance knobs for a network, so we develop a performance model for CNNs and present a method for using it to automatically determine efficient parallelization strategies. #R##N#We evaluate our algorithms with microbenchmarks and image classification with ResNet-50. Our algorithms allow us to prototype a model for a mesh-tangling dataset, where sample sizes are very large. We show that our parallelization achieves excellent strong and weak scaling and enables training for previously unreachable datasets.

Connected through hyperedge HE-1 (induced by source paper "hypar flow exploiting mpi and keras for scalable hybrid parallel dnn training using tensorflow"):
  with members:
    - "tensorflow eager a multi stage python embedded dsl for machine learning"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "beyond data and model parallelism for deep neural networks"
    - "regularized evolution for image classifier architecture search"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "mesh tensorflow deep learning for supercomputers"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "horovod fast and easy distributed deep learning in tensorflow"

Connected through hyperedge HE-2 (induced by source paper "predicting weather uncertainty with deep convnets"):
  with members:
    - "tackling climate change with machine learning"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "tensorflow a system for large scale machine 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)