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: "bridgeout stochastic bridge regularization for deep neural networks"
  Abstract: A major challenge in training deep neural networks is overfitting, i.e. inferior performance on unseen test examples compared to performance on training examples. To reduce overfitting, stochastic regularization methods have shown superior performance compared to deterministic weight penalties on a number of image recognition tasks. Stochastic methods such as Dropout and Shakeout, in expectation, are equivalent to imposing a ridge and elastic-net penalty on the model parameters, respectively. However, the choice of the norm of weight penalty is problem dependent and is not restricted to $\{L_1,L_2\}$. Therefore, in this paper we propose the Bridgeout stochastic regularization technique and prove that it is equivalent to an $L_q$ penalty on the weights, where the norm $q$ can be learned as a hyperparameter from data. Experimental results show that Bridgeout results in sparse model weights, improved gradients and superior classification performance compared to Dropout and Shakeout on synthetic and real datasets.

Connected through hyperedge HE-1 (induced by source paper "filter bank regularization of convolutional neural networks"):
  with members:
    - "gabor filter assisted energy efficient fast learning convolutional neural networks"
    - "learning transferable architectures for scalable image recognition"
    - "all you need is beyond a good init exploring better solution for training extremely deep convolutional neural networks with orthonormality and modulation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "sparseout controlling sparsity in deep networks"):
  with members:
    - "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"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "regularized deep networks in intelligent transportation systems a taxonomy and a case study"):
  with members:
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "exploring the landscape of spatial robustness"
    - "data driven color augmentation techniques for deep skin image analysis"
    - "fractalnet ultra deep neural networks without residuals"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "an overview of gradient descent optimization algorithms"
    - "robust convolutional neural networks under adversarial noise"
    - "very deep convolutional networks for large scale image recognition"

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)