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: "the incredible shrinking neural network new perspectives on learning representations through the lens of pruning"
  Abstract: How much can pruning algorithms teach us about the fundamentals of learning representations in neural networks? A lot, it turns out. Neural network model compression has become a topic of great interest in recent years, and many different techniques have been proposed to address this problem. In general, this is motivated by the idea that smaller models typically lead to better generalization. At the same time, the decision of what to prune and when to prune necessarily forces us to confront our assumptions about how neural networks actually learn to represent patterns in data. In this work we set out to test several long-held hypotheses about neural network learning representations and numerical approaches to pruning. To accomplish this we first reviewed the historical literature and derived a novel algorithm to prune whole neurons (as opposed to the traditional method of pruning weights) from optimally trained networks using a second-order Taylor method. We then set about testing the performance of our algorithm and analyzing the quality of the decisions it made. As a baseline for comparison we used a first-order Taylor method based on the Skeletonization algorithm and an exhaustive brute-force serial pruning algorithm. Our proposed algorithm worked well compared to a first-order method, but not nearly as well as the brute-force method. Our error analysis led us to question the validity of many widely-held assumptions behind pruning algorithms in general and the trade-offs we often make in the interest of reducing computational complexity. We discovered that there is a straightforward way, however expensive, to serially prune 40-70\% of the neurons in a trained network with minimal effect on the learning representation and without any re-training.

Connected through hyperedge HE-1 (induced by source paper "smaller models better generalization"):
  with members:
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "noiseout a simple way to prune neural networks"
    - "training sparse neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sparsifying neural network connections for face recognition"
    - "learning both weights and connections for efficient neural networks"
    - "deep learning in neural networks"

Connected through hyperedge HE-2 (induced by source paper "approximated oracle filter pruning for destructive cnn width optimization"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "learning to prune filters in convolutional neural networks"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "learning efficient convolutional networks through network slimming"
    - "nisp pruning networks using neuron importance score propagation"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-3 (induced by source paper "lifelong learning starting from zero"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "continual learning through synaptic intelligence"
    - "neurogenesis deep learning extending deep networks to accommodate new classes"
    - "overcoming catastrophic forgetting in neural networks"

Connected through hyperedge HE-4 (induced by source paper "leveraging filter correlations for deep model compression"):
  with members:
    - "hetconv heterogeneous kernel based convolutions for deep cnns"
    - "cpwc contextual point wise convolution for object recognition"
    - "play and prune adaptive filter pruning for deep model compression"
    - "stability based filter pruning for accelerating deep cnns"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "amc automl for model compression and acceleration on mobile devices"
    - "model compression via distillation and quantization"

Connected through hyperedge HE-5 (induced by source paper "channel wise pruning of neural networks with tapering resource constraint"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "progressive deep neural networks acceleration via soft filter pruning"
    - "systematic weight pruning of dnns using alternating direction method of multipliers"
    - "structured probabilistic pruning for convolutional neural network acceleration"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "towards thinner convolutional neural networks through gradually global pruning"

Connected through hyperedge HE-6 (induced by source paper "feta a dca pruning algorithm with generalization error guarantees"):
  with members:
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "loss aware binarization of deep networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorizing neural networks"

Connected through hyperedge HE-7 (induced by source paper "learning to prune deep neural networks via layer wise optimal brain surgeon"):
  with members:
    - "training skinny deep neural networks with iterative hard thresholding methods"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"
    - "pruning filters for efficient convnets"
    - "convolutional neural networks with low rank regularization"
    - "sparsifying neural network connections for face recognition"
    - "learning both weights and connections for efficient neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for 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.NE(Neural and Evolutionary Computing)