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: "memory bounded deep convolutional networks"
  Abstract: In this work, we investigate the use of sparsity-inducing regularizers during training of Convolution Neural Networks (CNNs). These regularizers encourage that fewer connections in the convolution and fully connected layers take non-zero values and in effect result in sparse connectivity between hidden units in the deep network. This in turn reduces the memory and runtime cost involved in deploying the learned CNNs. We show that training with such regularization can still be performed using stochastic gradient descent implying that it can be used easily in existing codebases. Experimental evaluation of our approach on MNIST, CIFAR, and ImageNet datasets shows that our regularizers can result in dramatic reductions in memory requirements. For instance, when applied on AlexNet, our method can reduce the memory consumption by a factor of four with minimal loss in accuracy.

Connected through hyperedge HE-1 (induced by source paper "centripetal sgd for pruning very deep convolutional networks with complicated structure"):
  with members:
    - "towards compact convnets via structure sparsity regularized filter pruning"
    - "autopruner an end to end trainable filter pruning method for efficient deep model inference"
    - "leveraging filter correlations for deep model compression"
    - "discrimination aware channel pruning for deep neural networks"
    - "a systematic dnn weight pruning framework using alternating direction method of multipliers"
    - "structured pruning for efficient convnets via incremental regularization"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "rethinking the value of network pruning"

Connected through hyperedge HE-2 (induced by source paper "neural epitome search for architecture agnostic network compression"):
  with members:
    - "partial order pruning for best speed accuracy trade off in neural architecture search"
    - "low bit quantization of neural networks for efficient inference"
    - "universally slimmable networks and improved training techniques"
    - "autoslim towards one shot architecture search for channel numbers"
    - "drop an octave reducing spatial redundancy in convolutional neural networks with octave convolution"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-3 (induced by source paper "adversarial neural pruning with latent vulnerability suppression"):
  with members:
    - "bridging adversarial robustness and gradient interpretability"
    - "theoretically principled trade off between robustness and accuracy"
    - "using pre training can improve model robustness and uncertainty"
    - "adv bnn improved adversarial defense through robust bayesian neural network"
    - "sparse dnns with improved adversarial robustness"
    - "a spectral view of adversarially robust features"
    - "arcface additive angular margin loss for deep face recognition"
    - "towards the first adversarially robust neural network model on mnist"

Connected through hyperedge HE-4 (induced by source paper "selective brain damage measuring the disparate impact of model pruning"):
  with members:
    - "natural adversarial examples"
    - "a benchmark for interpretability methods in deep neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the state of sparsity in deep neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "efficient neural audio synthesis"
    - "metric learning for novelty and anomaly detection"

Connected through hyperedge HE-5 (induced by source paper "deep learning at the edge"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "quantizing deep convolutional networks for efficient inference a whitepaper"
    - "pact parameterized clipping activation for quantized neural networks"
    - "deep learning in mobile and wireless networking a survey"
    - "deep learning for iot big data and streaming analytics a survey"
    - "wrpn wide reduced precision networks"
    - "hashnet deep learning to hash by continuation"
    - "distributed deep neural networks over the cloud the edge and end devices"

Connected through hyperedge HE-6 (induced by source paper "structured pruning of recurrent neural networks through neuron selection"):
  with members:
    - "centripetal sgd for pruning very deep convolutional networks with complicated structure"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "discrimination aware channel pruning for deep neural networks"
    - "compressing recurrent neural networks with tensor ring for action recognition"
    - "block sparse recurrent neural networks"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "learning intrinsic sparse structures within long short term memory"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-7 (induced by source paper "gasl guided attention for sparsity learning in deep neural networks"):
  with members:
    - "attention is all you need"
    - "learning structured sparsity in deep neural networks"
    - "attention correctness in neural image captioning"
    - "neural machine translation with supervised attention"
    - "guided alignment training for topic aware neural machine translation"
    - "supervised attentions for neural machine translation"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-8 (induced by source paper "parsimonious deep learning a differential inclusion approach with global convergence"):
  with members:
    - "growing a brain fine tuning by increasing model capacity"
    - "gradient descent finds global minima of deep neural networks"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "msplit lbi realizing feature selection and dense estimation simultaneously in few shot and zero shot learning"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "a convergence theory for deep learning via over parameterization"
    - "soft filter pruning for accelerating deep convolutional 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.CV(Computer Vision and Pattern Recognition)