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: "towards efficient convolutional neural network for domain specific applications on fpga"
  Abstract: FPGA becomes a popular technology for implementing Convolutional Neural Network (CNN) in recent years. Most CNN applications on FPGA are domain-specific, e.g., detecting objects from specific categories, in which commonly-used CNN models pre-trained on general datasets may not be efficient enough. This paper presents TuRF, an end-to-end CNN acceleration framework to efficiently deploy domain-specific applications on FPGA by transfer learning that adapts pre-trained models to specific domains, replacing standard convolution layers with efficient convolution blocks, and applying layer fusion to enhance hardware design performance. We evaluate TuRF by deploying a pre-trained VGG-16 model for a domain-specific image recognition task onto a Stratix V FPGA. Results show that designs generated by TuRF achieve better performance than prior methods for the original VGG-16 and ResNet-50 models, while for the optimised VGG-16 model TuRF designs are more accurate and easier to process.

Connected through hyperedge HE-1 (induced by source paper "embedded deep learning in ophthalmology making ophthalmic imaging smarter"):
  with members:
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "the intelligent icu pilot study using artificial intelligence technology for autonomous patient monitoring"
    - "towards safe deep learning accurately quantifying biomarker uncertainty in neural network predictions"
    - "an unsupervised learning model for deformable medical image registration"
    - "a deep learning approach to denoise optical coherence tomography images of the optic nerve head"
    - "plan3d viewpoint and trajectory optimization for aerial multi view stereo reconstruction"

Connected through hyperedge HE-2 (induced by source paper "a data center fpga acceleration platform for convolutional neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "vta an open hardware software stack for deep learning"
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (induced by source paper "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"):
  with members:
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "tvm end to end optimization stack for deep learning"
    - "yolov3 an incremental improvement"
    - "vta an open hardware software stack for deep learning"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "optimizing cnn model inference on cpus"

Connected through hyperedge HE-4 (induced by source paper "a survey of fpga based deep learning accelerators challenges and opportunities"):
  with members:
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "a survey of fpga based neural network accelerator"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "ternary weight networks"
    - "trained ternary quantization"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "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.CV(Computer Vision and Pattern Recognition)