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: "caffeinated fpgas fpga framework for convolutional neural networks"
  Abstract: Convolutional Neural Networks (CNNs) have gained significant traction in the field of machine learning, particularly due to their high accuracy in visual recognition. Recent works have pushed the performance of GPU implementations of CNNs to significantly improve their classification and training times. With these improvements, many frameworks have become available for implementing CNNs on both CPUs and GPUs, with no support for FPGA implementations. In this work we present a modified version of the popular CNN framework Caffe, with FPGA support. This allows for classification using CNN models and specialized FPGA implementations with the flexibility of reprogramming the device when necessary, seamless memory transactions between host and device, simple-to-use test benches, and the ability to create pipelined layer implementations. To validate the framework, we use the Xilinx SDAccel environment to implement an FPGA-based Winograd convolution engine and show that the FPGA layer can be used alongside other layers running on a host processor to run several popular CNNs (AlexNet, GoogleNet, VGG A, Overfeat). The results show that our framework achieves 50 GFLOPS across 3x3 convolutions in the benchmarks. This is achieved within a practical framework, which will aid in future development of FPGA-based CNNs.

Connected through hyperedge HE-1 (induced by source paper "gemmini an agile systolic array generator enabling systematic evaluations of deep learning architectures"):
  with members:
    - "integrating nvidia deep learning accelerator nvdla with risc v soc on firesim"
    - "fpsa a full system stack solution for reconfigurable reram based nn accelerator architecture"
    - "co design of deep neural nets and neural net accelerators for embedded vision applications"
    - "dnn dataflow choice is overrated"
    - "vta an open hardware software stack for deep learning"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"

Connected through hyperedge HE-2 (induced by source paper "fecaffe fpga enabled caffe with opencl for deep learning training and inference on intel stratix 10"):
  with members:
    - "a scalable framework for acceleration of cnn training on deeply pipelined fpga clusters with weight and workload balancing"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "efficient convolutional neural networks for pixelwise classification on heterogeneous hardware systems"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"):
  with members:
    - "approximate fpga based lstms under computation time constraints"
    - "in datacenter performance analysis of a tensor processing unit"
    - "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"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "fpga based cnn inference accelerator synthesized from multi threaded c software"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "learning to fly by crashing"

Connected through hyperedge HE-4 (induced by source paper "streaming architecture for large scale quantized neural networks on an fpga based dataflow platform"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "finn a framework for fast scalable binarized neural network inference"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "wide residual networks"

Connected through hyperedge HE-5 (induced by source paper "accelerating cnn inference on fpgas a survey"):
  with members:
    - "efficient processing of deep neural networks a tutorial and survey"
    - "vlsi implementation of deep neural network using integral stochastic computing"
    - "hardware oriented approximation of convolutional neural networks"
    - "ternary weight networks"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "trained ternary quantization"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-6 (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"
    - "an opencl tm deep learning accelerator on arria 10"
    - "multi residual networks improving the speed and accuracy of residual networks"
    - "hardware oriented approximation of 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-7 (induced by source paper "training for unstable cnn accelerator a case study on fpga"):
  with members:
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "fixed point quantization of deep convolutional networks"
    - "neural networks with few multiplications"
    - "caffe convolutional architecture for fast feature embedding"
    - "training deep neural networks with low precision multiplications"
    - "compressing deep convolutional networks using vector quantization"

Connected through hyperedge HE-8 (induced by source paper "sparse winograd convolutional neural networks on small scale systolic arrays"):
  with members:
    - "interpreting cnn knowledge via an explanatory graph"
    - "ssd single shot multibox detector"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fast algorithms for convolutional neural networks"
    - "very deep convolutional networks for large scale 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)