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: "learning neural network architectures using backpropagation"
  Abstract: Deep neural networks with millions of parameters are at the heart of many state of the art machine learning models today. However, recent works have shown that models with much smaller number of parameters can also perform just as well. In this work, we introduce the problem of architecture-learning, i.e; learning the architecture of a neural network along with weights. We introduce a new trainable parameter called tri-state ReLU, which helps in eliminating unnecessary neurons. We also propose a smooth regularizer which encourages the total number of neurons after elimination to be small. The resulting objective is differentiable and simple to optimize. We experimentally validate our method on both small and large networks, and show that it can learn models with a considerably small number of parameters without affecting prediction accuracy.

Connected through hyperedge HE-1 (induced by source paper "block wise dynamic sparseness"):
  with members:
    - "block sparse recurrent neural networks"
    - "you look twice gaternet for dynamic filter selection in cnns"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning efficient convolutional networks through network slimming"
    - "exploring sparsity in recurrent neural networks"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "language modeling with gated convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "multi objective pruning for cnns using genetic algorithm"):
  with members:
    - "learning structured sparsity in deep neural networks"
    - "training sparse neural networks"

Connected through hyperedge HE-3 (induced by source paper "the lottery ticket hypothesis finding sparse trainable neural networks"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "rethinking the value of network pruning"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep rewiring training very sparse deep networks"
    - "thinet a filter level pruning method for deep neural network compression"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "an end to end approach for speeding up neural network inference"):
  with members:
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "amc automl for model compression and acceleration on mobile devices"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "dynamic optimization of neural network structures using probabilistic modeling"
    - "rethinking the value of network pruning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "data driven sparse structure selection for deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "deep networks with probabilistic gates"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "taskonomy disentangling task transfer learning"
    - "network compression using correlation analysis of layer responses"
    - "rethinking the value of network pruning"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "data driven sparse structure selection for deep neural networks"
    - "snapshot ensembles train 1 get m for free"
    - "branchynet fast inference via early exiting from deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "dynamic optimization of neural network structures using probabilistic modeling"):
  with members:
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "learning structured sparsity in deep neural networks"
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"
    - "neural architecture search with reinforcement learning"
    - "swapout learning an ensemble of deep architectures"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "parameterless stochastic natural gradient method for discrete optimization and its application to hyper parameter optimization for neural network"):
  with members:
    - "dynamic optimization of neural network structures using probabilistic modeling"
    - "efficient neural architecture search via parameter sharing"
    - "a genetic programming approach to designing convolutional neural network architectures"
    - "densely connected convolutional networks"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "objective improvement in information geometric optimization"

Connected through hyperedge HE-8 (induced by source paper "data driven sparse structure selection for deep neural networks"):
  with members:
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "pelee a real time object detection system on mobile devices"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "large scale evolution of image classifiers"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

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)