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: "noiseout a simple way to prune neural networks"
  Abstract: Neural networks are usually over-parameterized with significant redundancy in the number of required neurons which results in unnecessary computation and memory usage at inference time. One common approach to address this issue is to prune these big networks by removing extra neurons and parameters while maintaining the accuracy. In this paper, we propose NoiseOut, a fully automated pruning algorithm based on the correlation between activations of neurons in the hidden layers. We prove that adding additional output neurons with entirely random targets results into a higher correlation between neurons which makes pruning by NoiseOut even more efficient. Finally, we test our method on various networks and datasets. These experiments exhibit high pruning rates while maintaining the accuracy of the original network.

Connected through hyperedge HE-1 (induced by source paper "full deep neural network training on a pruned weight budget"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "revisiting small batch training for deep neural networks"
    - "learning efficient convolutional networks through network slimming"
    - "cnvlutin2 ineffectual activation and weight free deep neural network computing"
    - "domain adaptive deep network compression"
    - "training quantized nets a deeper understanding"
    - "thinet a filter level pruning method for deep neural network compression"
    - "deep learning with low precision by half wave gaussian quantization"

Connected through hyperedge HE-2 (induced by source paper "smaller models better generalization"):
  with members:
    - "the incredible shrinking neural network new perspectives on learning representations through the lens of pruning"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "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-3 (induced by source paper "learning sparse networks using targeted dropout"):
  with members:
    - "the state of sparsity in deep neural networks"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "excitation dropout encouraging plasticity in deep neural networks"
    - "flipout efficient pseudo independent weight perturbations on mini batches"
    - "learning to prune filters in convolutional neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "rethinking the value of network pruning"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "a taxonomy of channel pruning signals in cnns"):
  with members:
    - "faster gaze prediction with dense networks and fisher pruning"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "pruning convolutional neural networks for resource efficient inference"
    - "network trimming a data driven neuron pruning approach towards efficient deep architectures"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "structured pruning of deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "adaptive ensemble of classifiers with regularization for imbalanced data classification"):
  with members:
    - "focal loss for dense object detection"
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "xgboost a scalable tree boosting system"
    - "understanding deep learning requires rethinking generalization"
    - "deep learning in neural networks"
    - "meta des"
    - "cost sensitive learning of deep feature representations from imbalanced data"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-6 (induced by source paper "mpdcompress matrix permutation decomposition algorithm for deep neural network compression"):
  with members:
    - "shakeout a new approach to regularized deep neural network training"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deeppicar a low cost deep neural network based autonomous car"
    - "structured deep neural network pruning via matrix pivoting"
    - "sep nets small and effective pattern networks"

Connected through hyperedge HE-7 (induced by source paper "effnet an efficient structure for convolutional neural networks"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "large scale evolution of image classifiers"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "learning to prune deep neural networks via layer wise optimal brain surgeon"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-8 (induced by source paper "understanding individual neuron importance using information theory"):
  with members:
    - "distilling critical paths in convolutional neural networks"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "revisiting the importance of individual units in cnns via ablation"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "ablation of a robot s brain neural networks under a knife"
    - "estimating information flow in neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "opening the black box of deep neural networks via information"

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)