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: "an opencl tm deep learning accelerator on arria 10"
  Abstract: Convolutional neural nets (CNNs) have become a practical means to perform vision tasks, particularly in the area of image classification. FPGAs are well known to be able to perform convolutions efficiently, however, most recent efforts to run CNNs on FPGAs have shown limited advantages over other devices such as GPUs. Previous approaches on FPGAs have often been memory bound due to the limited external memory bandwidth on the FPGA device. We show a novel architecture written in OpenCL(TM), which we refer to as a Deep Learning Accelerator (DLA), that maximizes data reuse and minimizes external memory bandwidth. Furthermore, we show how we can use the Winograd transform to significantly boost the performance of the FPGA. As a result, when running our DLA on Intel's Arria 10 device we can achieve a performance of 1020 img/s, or 23 img/s/W when running the AlexNet CNN benchmark. This comes to 1382 GFLOPs and is 10x faster with 8.4x more GFLOPS and 5.8x better efficiency than the state-of-the-art on FPGAs. Additionally, 23 img/s/W is competitive against the best publicly known implementation of AlexNet on nVidia's TitanX GPU.

Connected through hyperedge HE-1 (induced by source paper "lprnet license plate recognition via deep neural networks"):
  with members:
    - "segmentation free vehicle license plate recognition using convnet rnn"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "reading car license plates using deep convolutional neural networks and lstms"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "spatial transformer networks"
    - "parsenet looking wider to see better"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "face recognition with hybrid efficient convolution algorithms on fpgas"):
  with members:
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "multi residual networks improving the speed and accuracy of residual networks"
    - "hardware oriented approximation of convolutional neural networks"
    - "caffeinated fpgas fpga framework for convolutional neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "facenet a unified embedding for face recognition and clustering"
    - "fast algorithms for convolutional neural networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "a survey of fpga based neural network accelerator"):
  with members:
    - "design flow of accelerating hybrid extremely low bit width neural network in embedded fpga"
    - "mnasnet platform aware neural architecture search for mobile"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a gpu outperforming fpga accelerator architecture for binary convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"

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)