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: "yodann an architecture for ultra low power binary weight cnn acceleration"
  Abstract: Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy. The computational effort of today's CNNs requires power-hungry parallel processors or GP-GPUs. Recent developments in CNN accelerators for system-on-chip integration have reduced energy consumption significantly. Unfortunately, even these highly optimized devices are above the power envelope imposed by mobile and deeply embedded applications and face hard limitations caused by CNN weight I/O and storage. This prevents the adoption of CNNs in future ultra-low power Internet of Things end-nodes for near-sensor analytics. Recent algorithmic and theoretical advancements enable competitive classification accuracy even when limiting CNNs to binary (+1/-1) weights during training. These new findings bring major optimization opportunities in the arithmetic core by removing the need for expensive multiplications, as well as reducing I/O bandwidth and storage. In this work, we present an accelerator optimized for binary-weight CNNs that achieves 1510 GOp/s at 1.2 V on a core area of only 1.33 MGE (Million Gate Equivalent) or 0.19 mm$^2$ and with a power dissipation of 895 {\mu}W in UMC 65 nm technology at 0.6 V. Our accelerator significantly outperforms the state-of-the-art in terms of energy and area efficiency achieving 61.2 TOp/s/W@0.6 V and 1135 GOp/s/MGE@1.2 V, respectively.

Connected through hyperedge HE-1 (induced by source paper "sparsity in deep neural networks an empirical investigation with tensorquant"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "sparsenn an energy efficient neural network accelerator exploiting input and output sparsity"
    - "gradient sparsification for communication efficient distributed optimization"
    - "using gpi 2 for distributed memory paralleliziation of the caffe toolbox to speed up deep neural network training"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "sparse communication for distributed gradient descent"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"

Connected through hyperedge HE-2 (induced by source paper "an area and energy efficient design of domain wall memory based deep convolutional neural networks using stochastic computing"):
  with members:
    - "fft based deep learning deployment in embedded systems"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "hardware driven nonlinear activation for stochastic computing based deep convolutional neural networks"
    - "sc dcnn highly scalable deep convolutional neural network using stochastic computing"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "deep learning in neural networks"

Connected through hyperedge HE-3 (induced by source paper "an iot endpoint system on chip for secure and energy efficient near sensor analytics"):
  with members:
    - "origami a 803 gop s w convolutional network accelerator"
    - "a 128 channel extreme learning machine based neural decoder for brain machine interfaces"
    - "vlsi extreme learning machine a design space exploration"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "training deep neural networks with low precision multiplications"

Connected through hyperedge HE-4 (induced by source paper "quantized neural networks training neural networks with low precision weights and activations"):
  with members:
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "hardware oriented approximation of convolutional neural networks"
    - "bitwise neural networks"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "recurrent neural networks with limited numerical precision"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "convolutional neural networks using logarithmic data representation"

Connected through hyperedge HE-5 (induced by source paper "sc dcnn highly scalable deep convolutional neural network using stochastic computing"):
  with members:
    - "eie efficient inference engine on compressed deep neural network"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "reduced precision strategies for bounded memory in deep neural nets"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "computationally efficient target classification in multispectral image data with deep neural networks"):
  with members:
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "yodann an ultra low power convolutional neural network accelerator based on binary weights"
    - "fully convolutional networks for semantic segmentation"

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)