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: "a 0 3 2 6 tops w precision scalable processor for real time large scale convnets"
  Abstract: A low-power precision-scalable processor for ConvNets or convolutional neural networks (CNN) is implemented in a 40nm technology. Its 256 parallel processing units achieve a peak 102GOPS running at 204MHz. To minimize energy consumption while maintaining throughput, this works is the first to both exploit the sparsity of convolutions and to implement dynamic precision-scalability enabling supply- and energy scaling. The processor is fully C-programmable, consumes 25-288mW at 204 MHz and scales efficiency from 0.3-2.6 real TOPS/W. This system hereby outperforms the state-of-the-art up to 3.9x in energy efficiency.

Connected through hyperedge HE-1 (induced by source paper "ecnn a block based and highly parallel cnn accelerator for edge inference"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "morph flexible acceleration for 3d cnn based video understanding"
    - "euphrates algorithm soc co design for low power mobile continuous vision"
    - "scale sim systolic cnn accelerator simulator"
    - "eva 2 exploiting temporal redundancy in live computer vision"
    - "in datacenter performance analysis of a tensor processing unit"
    - "dslr quality photos on mobile devices with deep convolutional networks"

Connected through hyperedge HE-2 (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"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "deep residual learning for image recognition"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "a mixed signal architecture for convolutional neural networks"):
  with members:
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "understanding the impact of precision quantization on the accuracy and energy of neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "deep learning for iot big data and streaming analytics a survey"):
  with members:
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "enabling cognitive smart cities using big data and machine learning approaches and challenges"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an analysis of deep neural network models for practical applications"
    - "approxdbn approximate computing for discriminative deep belief networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "applying neural networks in optical communication systems possible pitfalls"

Connected through hyperedge HE-5 (induced by source paper "hardware for machine learning challenges and opportunities"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "hardware oriented approximation of convolutional neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "designing energy efficient convolutional neural networks using energy aware pruning"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-6 (induced by source paper "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "wrpn wide reduced precision networks"
    - "adaption toolbox and benchmark for training convolutional neural networks with reduced numerical precision weights and activation"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "steering a predator robot using a mixed frame event driven convolutional neural network"
    - "delta networks for optimized recurrent network computation"
    - "hardware oriented approximation of convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "multi mode inference engine for convolutional neural networks"):
  with members:
    - "chain nn an energy efficient 1d chain architecture for accelerating deep convolutional neural networks"
    - "origami a 803 gop s w convolutional network accelerator"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-8 (induced by source paper "algorithm and hardware design of discrete time spiking neural networks based on back propagation with binary activations"):
  with members:
    - "stdp based spiking deep convolutional neural networks for object recognition"
    - "the microsoft 2016 conversational speech recognition system"
    - "supervised learning based on temporal coding in spiking neural networks"
    - "training deep spiking neural networks using backpropagation"
    - "training spiking deep networks for neuromorphic hardware"
    - "converting static image datasets to spiking neuromorphic datasets using saccades"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

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.AR(Hardware Architecture)