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: "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
  Abstract: In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks. To accelerate the experimentation and development of CNNs, several software frameworks have been released, primarily targeting power-hungry CPUs and GPUs. In this context, reconfigurable hardware in the form of FPGAs constitutes a potential alternative platform that can be integrated in the existing deep learning ecosystem to provide a tunable balance between performance, power consumption and programmability. In this paper, a survey of the existing CNN-to-FPGA toolflows is presented, comprising a comparative study of their key characteristics which include the supported applications, architectural choices, design space exploration methods and achieved performance. Moreover, major challenges and objectives introduced by the latest trends in CNN algorithmic research are identified and presented. Finally, a uniform evaluation methodology is proposed, aiming at the comprehensive, complete and in-depth evaluation of CNN-to-FPGA toolflows.

Connected through hyperedge HE-1 (induced by source paper "recurrent neural networks an embedded computing perspective"):
  with members:
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "single stream parallelization of recurrent neural networks for low power and fast inference"
    - "effective quantization approaches for recurrent neural networks"
    - "alternating multi bit quantization for recurrent neural networks"
    - "a survey on methods and theories of quantized neural networks"
    - "finn l library extensions and design trade off analysis for variable precision lstm networks on fpgas"
    - "tensor decomposition for compressing recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "vargnet variable group convolutional neural network for efficient embedded computing"):
  with members:
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"
    - "panoptic feature pyramid networks"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "tvm end to end optimization stack for deep learning"

Connected through hyperedge HE-3 (induced by source paper "lutnet learning fpga configurations for highly efficient neural network inference"):
  with members:
    - "lutnet rethinking inference in fpga soft logic"
    - "focused quantization for sparse cnns"
    - "deep neural network approximation for custom hardware where we ve been where we re going"
    - "in datacenter performance analysis of a tensor processing unit"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "rebnet residual binarized neural network"
    - "finn a framework for fast scalable binarized neural network inference"

Connected through hyperedge HE-4 (induced by source paper "fixynn efficient hardware for mobile computer vision via transfer learning"):
  with members:
    - "fix your classifier the marginal value of training the last weight layer"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "towards closing the energy gap between hog and cnn features for embedded vision"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning multiple visual domains with residual adapters"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"

Connected through hyperedge HE-5 (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"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "towards efficient convolutional neural network for domain specific applications on fpga"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "optimizing cnn model inference on cpus"

Connected through hyperedge HE-6 (induced by source paper "evolutionary cell aided design for neural network architectures"):
  with members:
    - "regularized evolution for image classifier architecture search"
    - "exploration of low numeric precision deep learning inference using intel fpgas"
    - "large scale evolution of image classifiers"
    - "an analysis of deep neural network models for practical applications"
    - "learning transferable architectures for scalable image recognition"
    - "hierarchical representations for efficient architecture search"
    - "finn a framework for fast scalable binarized neural network inference"
    - "accelerating deep convolutional networks using low precision and sparsity"

Connected through hyperedge HE-7 (induced by source paper "a novel fpga based high throughput accelerator for binary search trees"):
  with members:
    - "evaluating built in ecc of fpga on chip memories for the mitigation of undervolting faults"
    - "accelerating cnn inference on fpgas a survey"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-8 (induced by source paper "deploying deep neural networks in the embedded space"):
  with members:
    - "f cnn text x a toolflow for mapping multiple convolutional neural networks on fpgas"
    - "approximate fpga based lstms under computation time constraints"
    - "dronet efficient convolutional neural network detector for real time uav applications"
    - "cascadecnn pushing the performance limits of quantisation"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "fpgaconvnet a toolflow for mapping diverse convolutional neural networks on embedded fpgas"
    - "mask r cnn"
    - "speed accuracy trade offs for modern convolutional object detectors"

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)