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: "on the importance of normalisation layers in deep learning with piecewise linear activation units"
  Abstract: Deep feedforward neural networks with piecewise linear activations are currently producing the state-of-the-art results in several public datasets. The combination of deep learning models and piecewise linear activation functions allows for the estimation of exponentially complex functions with the use of a large number of subnetworks specialized in the classification of similar input examples. During the training process, these subnetworks avoid overfitting with an implicit regularization scheme based on the fact that they must share their parameters with other subnetworks. Using this framework, we have made an empirical observation that can improve even more the performance of such models. We notice that these models assume a balanced initial distribution of data points with respect to the domain of the piecewise linear activation function. If that assumption is violated, then the piecewise linear activation units can degenerate into purely linear activation units, which can result in a significant reduction of their capacity to learn complex functions. Furthermore, as the number of model layers increases, this unbalanced initial distribution makes the model ill-conditioned. Therefore, we propose the introduction of batch normalisation units into deep feedforward neural networks with piecewise linear activations, which drives a more balanced use of these activation units, where each region of the activation function is trained with a relatively large proportion of training samples. Also, this batch normalisation promotes the pre-conditioning of very deep learning models. We show that by introducing maxout and batch normalisation units to the network in network model results in a model that produces classification results that are better than or comparable to the current state of the art in CIFAR-10, CIFAR-100, MNIST, and SVHN datasets.

Connected through hyperedge HE-1 (induced by source paper "supervised cosmos autoencoder learning beyond the euclidean loss"):
  with members:
    - "residual codean autoencoder for facial attribute analysis"
    - "semantic autoencoder for zero shot learning"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "relational autoencoder for feature extraction"
    - "improving facial attribute prediction using semantic segmentation"
    - "dynamic routing between capsules"
    - "age progression regression by conditional adversarial autoencoder"
    - "a good practice towards top performance of face recognition transferred deep feature fusion"

Connected through hyperedge HE-2 (induced by source paper "competition vs concatenation in skip connections of fully convolutional networks"):
  with members:
    - "quicknat segmenting mri neuroanatomy in 20 seconds"
    - "error corrective boosting for learning fully convolutional networks with limited data"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "understanding locally competitive networks"

Connected through hyperedge HE-3 (induced by source paper "competitive multi scale convolution"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to compare image patches via convolutional neural networks"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"
    - "understanding locally competitive networks"
    - "imagenet large scale visual recognition challenge"
    - "multi scale orderless pooling of deep convolutional activation features"

Connected through hyperedge HE-4 (induced by source paper "batch normalized maxout network in network"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "matconvnet convolutional neural networks for matlab"
    - "visualizing and understanding convolutional networks"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "from maxout to channel out encoding information on sparse pathways"
    - "improving neural networks by preventing co adaptation of feature detectors"

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)