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: "on the expressive power of overlapping architectures of deep learning"
  Abstract: Expressive efficiency refers to the relation between two architectures A and B, whereby any function realized by B could be replicated by A, but there exists functions realized by A, which cannot be replicated by B unless its size grows significantly larger. For example, it is known that deep networks are exponentially efficient with respect to shallow networks, in the sense that a shallow network must grow exponentially large in order to approximate the functions represented by a deep network of polynomial size. In this work, we extend the study of expressive efficiency to the attribute of network connectivity and in particular to the effect of "overlaps" in the convolutional process, i.e., when the stride of the convolution is smaller than its filter size (receptive field). To theoretically analyze this aspect of network's design, we focus on a well-established surrogate for ConvNets called Convolutional Arithmetic Circuits (ConvACs), and then demonstrate empirically that our results hold for standard ConvNets as well. Specifically, our analysis shows that having overlapping local receptive fields, and more broadly denser connectivity, results in an exponential increase in the expressive capacity of neural networks. Moreover, while denser connectivity can increase the expressive capacity, we show that the most common types of modern architectures already exhibit exponential increase in expressivity, without relying on fully-connected layers.

Connected through hyperedge HE-1 (induced by source paper "sparse weight activation training"):
  with members:
    - "improving neural network quantization without retraining using outlier channel splitting"
    - "swalp stochastic weight averaging in low precision training"
    - "scalable methods for 8 bit training of neural networks"
    - "mixed precision training of convolutional neural networks using integer operations"
    - "bridging the accuracy gap for 2 bit quantized neural networks qnn"
    - "training and inference with integers in deep neural networks"
    - "high accuracy low precision training"
    - "per tensor fixed point quantization of the back propagation algorithm"

Connected through hyperedge HE-2 (induced by source paper "efficientnet rethinking model scaling for convolutional neural networks"):
  with members:
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "do better imagenet models transfer better"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "domain adaptive transfer learning with specialist models"
    - "neural architecture optimization"

Connected through hyperedge HE-3 (induced by source paper "function approximation by deep networks"):
  with members:
    - "dimension independent bounds for general shallow networks"
    - "construction of neural networks for realization of localized deep learning"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions"
    - "bounding and counting linear regions of deep neural networks"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"

Connected through hyperedge HE-4 (induced by source paper "igcv3 interleaved low rank group convolutions for efficient deep neural networks"):
  with members:
    - "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"
    - "thinet a filter level pruning method for deep neural network compression"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "channel pruning for accelerating very deep neural networks"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"

Connected through hyperedge HE-5 (induced by source paper "analysis and design of convolutional networks via hierarchical tensor decompositions"):
  with members:
    - "tensorial mixture models"
    - "deep learning and quantum entanglement fundamental connections with implications to network design"
    - "boosting dilated convolutional networks with mixed tensor decompositions"
    - "densely connected convolutional networks"
    - "learning functions when is deep better than shallow"
    - "neural machine translation in linear time"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"

Connected through hyperedge HE-6 (induced by source paper "sum product quotient networks"):
  with members:
    - "neural networks and rational functions"
    - "inductive bias of deep convolutional networks through pooling geometry"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"
    - "pixel recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "a survey on latent tree models and applications"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-7 (induced by source paper "deep learning and quantum entanglement fundamental connections with implications to network design"):
  with members:
    - "tensorial mixture models"
    - "training input output recurrent neural networks through spectral methods"
    - "learning convolutional neural networks for graphs"
    - "deep convolutional networks on graph structured data"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "spectral networks and locally connected networks on graphs"

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)