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: "quantization and training of low bit width convolutional neural networks for object detection"
  Abstract: We present LBW-Net, an efficient optimization based method for quantization and training of the low bit-width convolutional neural networks (CNNs). Specifically, we quantize the weights to zero or powers of two by minimizing the Euclidean distance between full-precision weights and quantized weights during backpropagation. We characterize the combinatorial nature of the low bit-width quantization problem. For 2-bit (ternary) CNNs, the quantization of $N$ weights can be done by an exact formula in $O(N\log N)$ complexity. When the bit-width is three and above, we further propose a semi-analytical thresholding scheme with a single free parameter for quantization that is computationally inexpensive. The free parameter is further determined by network retraining and object detection tests. LBW-Net has several desirable advantages over full-precision CNNs, including considerable memory savings, energy efficiency, and faster deployment. Our experiments on PASCAL VOC dataset show that compared with its 32-bit floating-point counterpart, the performance of the 6-bit LBW-Net is nearly lossless in the object detection tasks, and can even do better in some real world visual scenes, while empirically enjoying more than 4$\times$ faster deployment.

Connected through hyperedge HE-1 (induced by source paper "understanding straight through estimator in training activation quantized neural nets"):
  with members:
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "deep neural nets with interpolating function as output activation"
    - "learning one convolutional layer with overlapping patches"
    - "loss aware weight quantization of deep networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "pact parameterized clipping activation for quantized neural networks"
    - "an optimal control approach to deep learning and applications to discrete weight neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "gdrq group based distribution reshaping for quantization"):
  with members:
    - "understanding straight through estimator in training activation quantized neural nets"
    - "syq learning symmetric quantization for efficient deep neural networks"
    - "haq hardware aware automated quantization with mixed precision"
    - "mixed precision quantization of convnets via differentiable neural architecture search"
    - "pact parameterized clipping activation for quantized neural networks"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-3 (induced by source paper "blended coarse gradient descent for full quantization of deep neural networks"):
  with members:
    - "deep neural nets with interpolating function as output activation"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "pact parameterized clipping activation for quantized neural networks"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "training quantized nets a deeper understanding"
    - "deep learning with low precision by half wave gaussian quantization"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"

Connected through hyperedge HE-4 (induced by source paper "proxquant quantized neural networks via proximal operators"):
  with members:
    - "alternating multi bit quantization for recurrent neural networks"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "loss aware weight quantization of deep networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "the high dimensional geometry of binary neural networks"
    - "training quantized nets a deeper understanding"
    - "model compression as constrained optimization with application to neural nets part ii quantization"
    - "model compression as constrained optimization with application to neural nets part i general framework"

Connected through hyperedge HE-5 (induced by source paper "binaryrelax a relaxation approach for training deep neural networks with quantized weights"):
  with members:
    - "stochastic layer wise precision in deep neural networks"
    - "extremely low bit neural network squeeze the last bit out with admm"
    - "training quantized nets a deeper understanding"
    - "model compression as constrained optimization with application to neural nets part ii quantization"
    - "model compression as constrained optimization with application to neural nets part i general framework"
    - "deep learning with low precision by half wave gaussian quantization"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "deep relaxation partial differential equations for optimizing deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "median binary connect method and a binary convolutional neural nework for word recognition"):
  with members:
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "binaryrelax a relaxation approach for training deep neural networks with quantized weights"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "bitnet bit regularized deep neural networks"):
  with members:
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "alternating direction method of multipliers for sparse convolutional neural networks"
    - "fixed point optimization of deep neural networks with adaptive step size retraining"
    - "two bit networks for deep learning on resource constrained embedded devices"
    - "towards the limit of network quantization"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "deep learning for real time crime forecasting and its ternarization"):
  with members:
    - "laplacian smoothing gradient descent"
    - "training quantized nets a deeper understanding"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "vnect real time 3d human pose estimation with a single rgb camera"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "gated graph sequence neural networks"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

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)