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: "deep learning in neural networks"
  Abstract: In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

Connected through hyperedge HE-1 (induced by source paper "π nets deep polynomial neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "polygan high order polynomial generators"
    - "on the convergence of adam and beyond"
    - "semantic image synthesis with spatially adaptive normalization"
    - "on the expressive power of deep polynomial neural networks"
    - "adversarial training of partially invertible variational autoencoders"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "residual networks of residual networks multilevel residual networks"

Connected through hyperedge HE-2 (induced by source paper "soft root sign activation function"):
  with members:
    - "mish a self regularized non monotonic neural activation function"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "improving deep learning by inverse square root linear units isrlus"
    - "invertible residual networks"
    - "the quest for the golden activation function"
    - "self normalizing neural networks"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method"):
  with members:
    - "bag of words as target for neural machine translation"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "neural system combination for machine translation"
    - "attention is all you need"
    - "nonparametric neural networks"
    - "sparse communication for distributed gradient descent"
    - "meprop sparsified back propagation for accelerated deep learning with reduced overfitting"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "improving coordination in small scale multi agent deep reinforcement learning through memory driven communication"):
  with members:
    - "learning to schedule communication in multi agent reinforcement learning"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "learning attentional communication for multi agent cooperation"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "tarmac targeted multi agent communication"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "actor attention critic for multi agent reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks"):
  with members:
    - "a survey of deep learning techniques for autonomous driving"
    - "neurall towards a unified model for visual perception in automated driving"
    - "yolov3 an incremental improvement"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "deep object centric policies for autonomous driving"
    - "recent advances in autoencoder based representation learning"
    - "advances in variational inference"
    - "explaining how a deep neural network trained with end to end learning steers a car"

Connected through hyperedge HE-6 (induced by source paper "translating diffusion wavelets and regularisation into residual networks"):
  with members:
    - "pde based group equivariant convolutional neural networks"
    - "regularization by architecture a deep prior approach for inverse problems"
    - "analysis of invariance and robustness via invertibility of relu networks"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"
    - "networks for nonlinear diffusion problems in imaging"
    - "wavelet convolutional neural networks"
    - "forward stability of resnet and its variants"

Connected through hyperedge HE-7 (induced by source paper "nnoculation broad spectrum and targeted treatment of backdoored dnns"):
  with members:
    - "are adversarial perturbations a showstopper for ml based cad a case study on cnn based lithographic hotspot detection"
    - "strip a defence against trojan attacks on deep neural networks"
    - "defending neural backdoors via generative distribution modeling"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "generative adversarial networks an overview"
    - "spectral signatures in backdoor attacks"
    - "fastmri an open dataset and benchmarks for accelerated mri"

Connected through hyperedge HE-8 (induced by source paper "forecastnet a time variant deep feed forward neural network architecture for multi step ahead time series forecasting"):
  with members:
    - "deep transformer models for time series forecasting the influenza prevalence case"
    - "recurrent neural networks for time series forecasting current status and future directions"
    - "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"
    - "foundations of sequence to sequence modeling for time series"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "autoregressive convolutional neural networks for asynchronous time series"
    - "wavenet a generative model for raw audio"

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)