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: "wrpn training and inference using wide reduced precision networks"
  Abstract: For computer vision applications, prior works have shown the efficacy of reducing the numeric precision of model parameters (network weights) in deep neural networks but also that reducing the precision of activations hurts model accuracy much more than reducing the precision of model parameters. We study schemes to train networks from scratch using reduced-precision activations without hurting the model accuracy. We reduce the precision of activation maps (along with model parameters) using a novel quantization scheme and increase the number of filter maps in a layer, and find that this scheme compensates or surpasses the accuracy of the baseline full-precision network. As a result, one can significantly reduce the dynamic memory footprint, memory bandwidth, computational energy and speed up the training and inference process with appropriate hardware support. We call our scheme WRPN - wide reduced-precision networks. We report results using our proposed schemes and show that our results are better than previously reported accuracies on ILSVRC-12 dataset while being computationally less expensive compared to previously reported reduced-precision networks.

Connected through hyperedge HE-1 (induced by source paper "xnor net improved binary neural networks"):
  with members:
    - "hierarchical binary cnns for landmark localization with limited resources"
    - "t net parametrizing fully convolutional nets with a single high order tensor"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "training competitive binary neural networks from scratch"
    - "towards accurate binary convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "matrix and tensor decompositions for training binary neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "multi scale structure aware network for human pose estimation"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "cp decomposition with tensor power method for convolutional neural networks compression"
    - "tensor regression networks"
    - "learning feature pyramids for human pose estimation"
    - "towards accurate binary convolutional neural network"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-3 (induced by source paper "constructing energy efficient mixed precision neural networks through principal component analysis for edge intelligence"):
  with members:
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "per tensor fixed point quantization of the back propagation algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "a low effort approach to structured cnn design using pca"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "hybrid binary networks optimizing for accuracy efficiency and memory"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "pact parameterized clipping activation for quantized neural networks"

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.LG(Machine Learning)