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: "blocking and sparsity for optimization of convolution calculation algorithm on gpus"
  Abstract: Convolution neural network (CNN) plays a paramount role in machine learning, which has made significant contributions, such as medical image classification, natural language processing, and recommender system. The success convolution neural network achieved excellent performance with fast execution time. Due to the convolution operation dominate the total operation time of Convolution neural network. In this paper, we propose a novel convolution method of Graphic Processing Units (GPUs), which reduce the convolution operation time and improve the execution speed approximately 2X than the state of the art convolution algorithm. Our work based on the observation is that the sparsity of the input feature map of convolution operation is relatively large, and the zero value of the feature map is redundancy for convolution result. Therefore, we skip the zero value calculation and improve the speed by compressing the feature map. Besides, the shape of the feature map for the deep network is small, and the number of threads is limited. Therefore, for a limited number of threads, it is necessary to reduce the amount of calculation to increase the calculation speed. Our algorithm has a good effect on the convolution operation of the feature map of the deep network with large sparsity and small size. #R##N#In this work, our contributions can be summarized as follows: 1) A novel store format for hight-sparsity feature map. 2) A novel convolution algorithm based on block compression and Shared memory is proposed. 3) A feature map data-set for convolution algorithm optimization. 4) We performed a single-layer convolution comparison experiment with CuDNN for different models, and it is best to achieve 3.5X speedup. We also implemented the algorithm on the VGG-19 model, which can achieve 1.3X$\sim$2.9X speedup in deep convolution operation, and the entire network can achieve 2.3X speedup.

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)