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: "characterizing sparse connectivity patterns in neural networks"
  Abstract: We propose a novel way of reducing the number of parameters in the storage-hungry fully connected layers of a neural network by using pre-defined sparsity, where the majority of connections are absent prior to starting training. Our results indicate that convolutional neural networks can operate without any loss of accuracy at less than half percent classification layer connection density, or less than 5 percent overall network connection density. We also investigate the effects of pre-defining the sparsity of networks with only fully connected layers. Based on our sparsifying technique, we introduce the ‘scatter’ metric to characterize the quality of a particular connection pattern. As proof of concept, we show results on CIFAR, MNIST and a new dataset on classifying Morse code symbols, which highlights some interesting trends and limits of sparse connection patterns.

Connected through hyperedge HE-1 (induced by source paper "pre defined sparse neural networks with hardware acceleration"):
  with members:
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "recursive binary neural network learning model with 2 bit weight storage requirement"
    - "morse code datasets for machine learning"
    - "a highly parallel fpga implementation of sparse neural network training"
    - "revisiting small batch training for deep neural networks"
    - "accelerating training of deep neural networks via sparse edge processing"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep expander networks efficient deep networks from graph theory"

Connected through hyperedge HE-2 (induced by source paper "group connected multilayer perceptron networks"):
  with members:
    - "dynamic feature acquisition using denoising autoencoders"
    - "nutrition and health data for cost sensitive learning"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "feature grouping as a stochastic regularizer for high dimensional structured data"
    - "learning sparse neural networks via sensitivity driven regularization"
    - "how powerful are graph neural networks"
    - "self normalizing neural networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "morse code datasets for machine learning"):
  with members:
    - "accelerating training of deep neural networks via sparse edge processing"
    - "toward geometric deep slam"
    - "using synthetic data to train neural networks is model based reasoning"
    - "interleaver design for deep neural networks"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-4 (induced by source paper "a highly parallel fpga implementation of sparse neural network training"):
  with members:
    - "accelerating training of deep neural networks via sparse edge processing"
    - "interleaver design for deep neural networks"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "understanding deep learning requires rethinking generalization"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "interleaver design for deep neural networks"):
  with members:
    - "accelerating training of deep neural networks via sparse edge processing"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"
    - "very deep convolutional networks for large scale image recognition"

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)