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: "releq an automatic reinforcement learning approach for deep quantization of neural networks"
  Abstract: Deep Neural Networks (DNNs) typically require massive amount of computation resource in inference tasks for computer vision applications. Quantization can significantly reduce DNN computation and storage by decreasing the bitwidth of network encodings. Recent research affirms that carefully selecting the quantization levels for each layer can preserve the accuracy while pushing the bitwidth below eight bits. However, without arduous manual effort, this deep quantization can lead to significant accuracy loss, leaving it in a position of questionable utility. As such, deep quantization opens a large hyper-parameter space (bitwidth of the layers), the exploration of which is a major challenge. We propose a systematic approach to tackle this problem, by automating the process of discovering the quantization levels through an end-to-end deep reinforcement learning framework (ReLeQ). We adapt policy optimization methods to the problem of quantization, and focus on finding the best design decisions in choosing the state and action spaces, network architecture and training framework, as well as the tuning of various hyperparamters. We show how ReLeQ can balance speed and quality, and provide an asymmetric general solution for quantization of a large variety of deep networks (AlexNet, CIFAR-10, LeNet, MobileNet-V1, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy (=< 0.3% loss) while minimizing the computation and storage cost. With these DNNs, ReLeQ enables conventional hardware to achieve 2.2x speedup over 8-bit execution. Similarly, a custom DNN accelerator achieves 2.0x speedup and energy reduction compared to 8-bit runs. These encouraging results mark ReLeQ as the initial step towards automating the deep quantization of neural networks.

Connected through hyperedge HE-1 (induced by source paper "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices"):
  with members:
    - "exploring randomly wired neural networks for image recognition"
    - "automatic model selection for neural networks"
    - "swiftnet using graph propagation as meta knowledge to search highly representative neural architectures"
    - "discovering neural wirings"
    - "pytorch an imperative style high performance deep learning library"
    - "learned step size quantization"
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "amc automl for model compression and acceleration on mobile devices"

Connected through hyperedge HE-2 (induced by source paper "efficient bitwidth search for practical mixed precision neural network"):
  with members:
    - "differentiable soft quantization bridging full precision and low bit neural networks"
    - "hawq hessian aware quantization of neural networks with mixed precision"
    - "regularized evolution for image classifier architecture search"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "discrimination aware channel pruning for deep neural networks"
    - "haq hardware aware automated quantization with mixed precision"

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"
    - "trained quantization thresholds for accurate and efficient fixed point inference of deep neural networks"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "adabits neural network quantization with adaptive bit widths"):
  with members:
    - "a survey of deep learning techniques for autonomous driving"
    - "autogan neural architecture search for generative adversarial networks"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "detnas backbone search for object detection"
    - "differentiable quantization of deep neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "autoqb automl for network quantization and binarization on mobile devices"
    - "once for all train one network and specialize it for efficient deployment on diverse hardware platforms"

Connected through hyperedge HE-5 (induced by source paper "automatic neural network compression by sparsity quantization joint learning a constrained optimization based approach"):
  with members:
    - "single path one shot neural architecture search with uniform sampling"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "amc automl for model compression and acceleration on mobile devices"
    - "ecc energy constrained deep neural network compression via a bilinear regression model"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "energy constrained compression for deep neural networks via weighted sparse projection and layer input masking"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "towards learning of filter level heterogeneous compression of convolutional neural networks"):
  with members:
    - "improving neural architecture search image classifiers via ensemble learning"
    - "single path nas designing hardware efficient convnets in less than 4 hours"
    - "regularizing activation distribution for training binarized deep networks"
    - "metapruning meta learning for automatic neural network channel pruning"
    - "multi objective reinforced evolution in mobile neural architecture search"
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "nas bench 101 towards reproducible neural architecture search"
    - "single path one shot neural architecture search with uniform sampling"

Connected through hyperedge HE-7 (induced by source paper "differentiable soft quantization bridging full precision and low bit neural networks"):
  with members:
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "blended coarse gradient descent for full quantization of deep neural networks"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "learning to quantize deep networks by optimizing quantization intervals with task loss"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-8 (induced by source paper "gross group size series decomposition for whole search space training"):
  with members:
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "darts differentiable architecture search"
    - "mnasnet platform aware neural architecture search for mobile"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "aggregated residual transformations for 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.LG(Machine Learning)