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: "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
  Abstract: Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving many state-of-the-art (SOA) visual processing tasks. Even though Graphical Processing Units (GPUs) are most often used in training and deploying CNNs, their power efficiency is less than 10 GOp/s/W for single-frame runtime inference. We propose a flexible and efficient CNN accelerator architecture called NullHop that implements SOA CNNs useful for low-power and low-latency application scenarios. NullHop exploits the sparsity of neuron activations in CNNs to accelerate the computation and reduce memory requirements. The flexible architecture allows high utilization of available computing resources across kernel sizes ranging from 1x1 to 7x7. NullHop can process up to 128 input and 128 output feature maps per layer in a single pass. We implemented the proposed architecture on a Xilinx Zynq FPGA platform and present results showing how our implementation reduces external memory transfers and compute time in five different CNNs ranging from small ones up to the widely known large VGG16 and VGG19 CNNs. Post-synthesis simulations using Mentor Modelsim in a 28nm process with a clock frequency of 500 MHz show that the VGG19 network achieves over 450 GOp/s. By exploiting sparsity, NullHop achieves an efficiency of 368%, maintains over 98% utilization of the MAC units, and achieves a power efficiency of over 3TOp/s/W in a core area of 6.3mm$^2$. As further proof of NullHop's usability, we interfaced its FPGA implementation with a neuromorphic event camera for real time interactive demonstrations.

Connected through hyperedge HE-1 (induced by source paper "ebpc extended bit plane compression for deep neural network inference and training accelerators"):
  with members:
    - "and the bit goes down revisiting the quantization of neural networks"
    - "additive noise annealing and approximation properties of quantized neural networks"
    - "a 64 mw dnn based visual navigation engine for autonomous nano drones"
    - "rpr random partition relaxation for training binary and ternary weight neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "multiposenet fast multi person pose estimation using pose residual network"

Connected through hyperedge HE-2 (induced by source paper "high speed and high dynamic range video with an event camera"):
  with members:
    - "events to video bringing modern computer vision to event cameras"
    - "ced color event camera dataset"
    - "event based vision a survey"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "yolov3 an incremental improvement"
    - "the multi vehicle stereo event camera dataset an event camera dataset for 3d perception"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-3 (induced by source paper "comprehensive snn compression using admm optimization and activity regularization"):
  with members:
    - "restocnet residual stochastic binary convolutional spiking neural network for memory efficient neuromorphic computing"
    - "admm nn an algorithm hardware co design framework of dnns using alternating direction method of multipliers"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "training and inference with integers in deep neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "progressive weight pruning of deep neural networks using admm"
    - "spiking deep residual network"

Connected through hyperedge HE-4 (induced by source paper "hardnet a low memory traffic network"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "scale sim systolic cnn accelerator simulator"
    - "sparsely aggregated convolutional networks"
    - "single shot refinement neural network for object detection"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-5 (induced by source paper "a camera that cnns towards embedded neural networks on pixel processor arrays"):
  with members:
    - "ternary weight networks"
    - "accelerating deep convolutional networks using low precision and sparsity"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "trained ternary quantization"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "rounding methods for neural networks with low resolution synaptic weights"
    - "neural networks with few multiplications"

Connected through hyperedge HE-6 (induced by source paper "neupart using analytical models to drive energy efficient partitioning of cnn computations on cloud connected mobile clients"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "using deep learning for image based plant disease detection"
    - "hardware oriented approximation of convolutional neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "dynamic vision sensor integration on fpga based cnn accelerators for high speed visual classification"):
  with members:
    - "ai benchmark running deep neural networks on android smartphones"
    - "performance evaluation over hw sw co design soc memory transfers for a cnn accelerator"
    - "in datacenter performance analysis of a tensor processing unit"
    - "a 0 3 2 6 tops w precision scalable processor for real time large scale convnets"
    - "deep residual learning for image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "the importance of space and time in neuromorphic cognitive agents"):
  with members:
    - "a 0 086 mm 2 12 7 pj sop 64k synapse 256 neuron online learning digital spiking neuromorphic processor in 28 nm cmos"
    - "an ultralow leakage synaptic scaling homeostatic plasticity circuit with configurable time scales up to 100 ks"
    - "adaption toolbox and benchmark for training convolutional neural networks with reduced numerical precision weights and activation"

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)