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: "learned norm pooling for deep feedforward and recurrent neural networks"
  Abstract: In this paper we propose and investigate a novel nonlinear unit, called $L_p$ unit, for deep neural networks. The proposed $L_p$ unit receives signals from several projections of a subset of units in the layer below and computes a normalized $L_p$ norm. We notice two interesting interpretations of the $L_p$ unit. First, the proposed unit can be understood as a generalization of a number of conventional pooling operators such as average, root-mean-square and max pooling widely used in, for instance, convolutional neural networks (CNN), HMAX models and neocognitrons. Furthermore, the $L_p$ unit is, to a certain degree, similar to the recently proposed maxout unit (Goodfellow et al., 2013) which achieved the state-of-the-art object recognition results on a number of benchmark datasets. Secondly, we provide a geometrical interpretation of the activation function based on which we argue that the $L_p$ unit is more efficient at representing complex, nonlinear separating boundaries. Each $L_p$ unit defines a superelliptic boundary, with its exact shape defined by the order $p$. We claim that this makes it possible to model arbitrarily shaped, curved boundaries more efficiently by combining a few $L_p$ units of different orders. This insight justifies the need for learning different orders for each unit in the model. We empirically evaluate the proposed $L_p$ units on a number of datasets and show that multilayer perceptrons (MLP) consisting of the $L_p$ units achieve the state-of-the-art results on a number of benchmark datasets. Furthermore, we evaluate the proposed $L_p$ unit on the recently proposed deep recurrent neural networks (RNN).

Connected through hyperedge HE-1 (induced by source paper "weakly supervised segmentation of cracks on solar cells using normalized lp norm"):
  with members:
    - "automatic classification of defective photovoltaic module cells in electroluminescence images"
    - "segmentation of photovoltaic module cells in electroluminescence images"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "cnn image retrieval learns from bow unsupervised fine tuning with hard examples"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "lip local importance based pooling"):
  with members:
    - "an empirical study of spatial attention mechanisms in deep networks"
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "scale aware trident networks for object detection"
    - "bag of tricks for image classification with convolutional neural networks"
    - "detnet a backbone network for object detection"
    - "grid r cnn"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "detail preserving pooling in deep networks"

Connected through hyperedge HE-3 (induced by source paper "frank wolfe network an interpretable deep structure for non sparse coding"):
  with members:
    - "learning simple thresholded features with sparse support recovery"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a unified optimization view on generalized matching pursuit and frank wolfe"
    - "maximal sparsity with deep networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "normalizing the normalizers comparing and extending network normalization schemes"

Connected through hyperedge HE-4 (induced by source paper "thick net parallel network structure for sequential modeling"):
  with members:
    - "going wider recurrent neural network with parallel cells"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "linguistically regularized lstms for sentiment classification"
    - "tying word vectors and word classifiers a loss framework for language modeling"
    - "recurrent dropout without memory loss"
    - "recurrent highway networks"

Connected through hyperedge HE-5 (induced by source paper "sparseout controlling sparsity in deep networks"):
  with members:
    - "bridgeout stochastic bridge regularization for deep neural networks"
    - "regularizing and optimizing lstm language models"
    - "sparse activity and sparse connectivity in supervised learning"
    - "wide residual networks"
    - "pointer sentinel mixture models"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "a critical review of recurrent neural networks for sequence learning"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-6 (induced by source paper "ensemble kalman inversion a derivative free technique for machine learning tasks"):
  with members:
    - "self normalizing neural networks"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "uncertainty quantification in graph based classification of high dimensional data"
    - "a survey on deep learning in medical image analysis"
    - "autoregressive convolutional neural networks for asynchronous time series"
    - "instance normalization the missing ingredient for fast stylization"
    - "entropy sgd biasing gradient descent into wide valleys"

Connected through hyperedge HE-7 (induced by source paper "l p norm constrained coding with frank wolfe network"):
  with members:
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "maximal sparsity with deep networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "flowing convnets for human pose estimation in videos"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-8 (induced by source paper "ordinal pooling networks for preserving information over shrinking feature maps"):
  with members:
    - "a fully trainable network with rnn based pooling"
    - "squeeze and excitation networks"
    - "seed expand and constrain three principles for weakly supervised image segmentation"
    - "s3pool pooling with stochastic spatial sampling"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "striving for simplicity the all convolutional net"
    - "fractional max pooling"

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)