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: "snowflake a model agnostic accelerator for convolutional neural networks"
  Abstract: Deep convolutional neural networks (CNNs) are the deep learning model of choice for performing object detection, classification, semantic segmentation and natural language processing tasks. CNNs require billions of operations to process a frame. This computational complexity, combined with the inherent parallelism of the convolution operation make CNNs an excellent target for custom accelerators. However, when optimizing for different CNN hierarchies and data access patterns, it is difficult for custom accelerators to achieve close to 100% computational efficiency. In this work, we present Snowflake, a scalable and efficient accelerator that is agnostic to CNN workloads, and was designed to always perform at near-peak hardware utilization. Snowflake is able to achieve a computational efficiency of over 91% on modern CNN models. Snowflake, implemented on a Xilinx Zynq XC7Z045 SoC is capable of achieving a peak throughput of 128G-ops/s and a measured throughput of 100 frames per second and 120 G-ops/s on the AlexNet CNN model, 36 frames per second and 116G- ops/s on the GoogLeNet CNN model and 17 frames per second and 122 G-ops/s on the ResNet-50 CNN model. To the best of our knowledge, Snowflake is the only implemented system capable of achieving over 91% efficiency on modern CNNs and the only implemented system with GoogLeNet and ResNet as part of the benchmark suite.

Connected through hyperedge HE-1 (induced by source paper "high performance scalable fpga accelerator for deep neural networks"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "ternary neural networks with fine grained quantization"
    - "hardware oriented approximation of convolutional neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "trained ternary quantization"
    - "neural networks with few multiplications"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "tensorquant a simulation toolbox for deep neural network quantization"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "training quantized nets a deeper understanding"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "eie efficient inference engine on compressed deep neural network"
    - "convolutional neural networks using logarithmic data representation"
    - "trained ternary quantization"

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)