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: "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
  Abstract: We propose a method of training quantization thresholds (TQT) for uniform symmetric quantizers using standard backpropagation and gradient descent. Contrary to prior work, we show that a careful analysis of the straight-through estimator for threshold gradients allows for a natural range-precision trade-off leading to better optima. Our quantizers are constrained to use power-of-2 scale-factors and per-tensor scaling of weights and activations to make it amenable for hardware implementations. We present analytical support for the general robustness of our methods and empirically validate them on various CNNs for ImageNet classification. We are able to achieve near-floating-point accuracy on traditionally difficult networks such as MobileNets with less than 5 epochs of quantized (8-bit) retraining. Finally, we present Graffitist, a framework that enables automatic quantization of TensorFlow graphs for TQT. Available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "neural network compression framework for fast model inference"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "learning sparse networks using targeted dropout"
    - "the state of sparsity in deep neural networks"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "pact parameterized clipping activation for quantized neural networks"
    - "mnasnet platform aware neural architecture search for mobile"
    - "progressive neural architecture search"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-2 (induced by source paper "low memory neural network training a technical report"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning fast algorithms for linear transforms using butterfly factorizations"
    - "memory efficient adaptive optimization"
    - "sparse evolutionary deep learning with over one million artificial neurons on commodity hardware"
    - "fixup initialization residual learning without normalization"
    - "full deep neural network training on a pruned weight budget"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"

Connected through hyperedge HE-3 (induced by source paper "towards efficient training for neural network quantization"):
  with members:
    - "effective training of convolutional neural networks with low bitwidth weights and activations"
    - "a mean field theory of batch normalization"
    - "differentiable quantization of deep neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "autoqb automl for network quantization and binarization on mobile devices"
    - "fixup initialization residual learning without normalization"
    - "learned step size quantization"
    - "binary ensemble neural network more bits per network or more networks per bit"

Connected through hyperedge HE-4 (induced by source paper "qkd quantization aware knowledge distillation"):
  with members:
    - "data free quantization through weight equalization and bias correction"
    - "differentiable quantization of deep neural networks"
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "feature fusion for online mutual knowledge distillation"
    - "efficient 8 bit quantization of transformer neural machine language translation model"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learned step size quantization"

Connected through hyperedge HE-5 (induced by source paper "memory driven mixed low precision quantization for enabling deep network inference on microcontrollers"):
  with members:
    - "low bit quantization of neural networks for efficient inference"
    - "learning low precision neural networks without straight through estimator ste"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "ifq net integrated fixed point quantization networks for embedded vision"
    - "learned step size quantization"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "fbnet hardware aware efficient convnet design via differentiable neural architecture search"
    - "lq nets learned quantization for highly accurate and compact deep 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.CV(Computer Vision and Pattern Recognition)