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: "batch normalized maxout network in network"
  Abstract: This paper reports a novel deep architecture referred to as Maxout network In Network (MIN), which can enhance model discriminability and facilitate the process of information abstraction within the receptive field. The proposed network adopts the framework of the recently developed Network In Network structure, which slides a universal approximator, multilayer perceptron (MLP) with rectifier units, to exact features. Instead of MLP, we employ maxout MLP to learn a variety of piecewise linear activation functions and to mediate the problem of vanishing gradients that can occur when using rectifier units. Moreover, batch normalization is applied to reduce the saturation of maxout units by pre-conditioning the model and dropout is applied to prevent overfitting. Finally, average pooling is used in all pooling layers to regularize maxout MLP in order to facilitate information abstraction in every receptive field while tolerating the change of object position. Because average pooling preserves all features in the local patch, the proposed MIN model can enforce the suppression of irrelevant information during training. Our experiments demonstrated the state-of-the-art classification performance when the MIN model was applied to MNIST, CIFAR-10, and CIFAR-100 datasets and comparable performance for SVHN dataset.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning with linear quadratic regulator regions"):
  with members:
    - "deep drone racing from simulation to reality with domain randomization"
    - "solving rubik s cube with a robot hand"
    - "assessing transferability from simulation to reality for reinforcement learning"
    - "continuous control with contexts provably"
    - "learning agile and dynamic motor skills for legged robots"
    - "h_ infty model free reinforcement learning with robust stability guarantee"
    - "robust model free reinforcement learning with multi objective bayesian optimization"
    - "sensor transfer learning optimal sensor effect image augmentation for sim to real domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "a branching and merging convolutional network with homogeneous filter capsules"):
  with members:
    - "homogeneous vector capsules enable adaptive gradient descent in convolutional neural networks"
    - "rmdl random multimodel deep learning for classification"
    - "averaging weights leads to wider optima and better generalization"
    - "random erasing data augmentation"
    - "dynamic routing between capsules"
    - "lets keep it simple using simple architectures to outperform deeper and more complex architectures"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "striking the right balance with uncertainty"):
  with members:
    - "max margin class imbalanced learning with gaussian affinity"
    - "zero shot object detection learning to simultaneously recognize and localize novel concepts"
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "learning to reweight examples for robust deep learning"
    - "additive margin softmax for face verification"
    - "linear maximum margin classifier for learning from uncertain data"
    - "feature transfer learning for deep face recognition with under represented data"

Connected through hyperedge HE-4 (induced by source paper "max margin class imbalanced learning with gaussian affinity"):
  with members:
    - "cosface large margin cosine loss for deep face recognition"
    - "arcface additive angular margin loss for deep face recognition"
    - "feature transfer learning for deep face recognition with under represented data"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "sphereface deep hypersphere embedding for face recognition"
    - "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"
    - "prototypical networks for few shot learning"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "neighborhood watch representation learning with local margin triplet loss and sampling strategy for k nearest neighbor image classification"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "in defense of the triplet loss for person re identification"
    - "survivalnet predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3d convolutional neural networks"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "deep image retrieval learning global representations for image search"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "learning local image descriptors with deep siamese and triplet convolutional networks by minimising global loss functions"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "unbounded output networks for classification"):
  with members:
    - "sigmoid weighted linear units for neural network function approximation in reinforcement learning"
    - "swish a self gated activation function"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "fractalnet ultra deep neural networks without residuals"
    - "all you need is a good init"

Connected through hyperedge HE-7 (induced by source paper "doing the impossible why neural networks can be trained at all"):
  with members:
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "self normalizing neural networks"
    - "assessing the linguistic productivity of unsupervised deep neural networks"
    - "opening the black box of deep neural networks via information"
    - "the power of depth for feedforward neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-8 (induced by source paper "assessing four neural networks on handwritten digit recognition dataset mnist"):
  with members:
    - "adanet adaptive structural learning of artificial neural networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "object detection networks on convolutional feature maps"
    - "multiple object recognition with visual attention"
    - "multi digit number recognition from street view imagery using deep convolutional neural networks"
    - "comparison and combination of state of the art techniques for handwritten character recognition topping the mnist benchmark"

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)