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 big simple neural nets for handwritten digit recognition"
  Abstract: Good old online backpropagation for plain multilayer perceptrons yields a very low 0.35% error rate on the MNIST handwritten digits benchmark. All we need to achieve this best result so far are many hidden layers, many neurons per layer, numerous deformed training images to avoid overfitting, and graphics cards to greatly speed up learning.

Connected through hyperedge HE-1 (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-2 (induced by source paper "the value of nullspace tuning using partial label information"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "coronary calcium detection using 3d attention identical dual deep network based on weakly supervised learning"
    - "three mechanisms of weight decay regularization"
    - "inter scanner harmonization of high angular resolution dw mri using null space deep learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-3 (induced by source paper "simex express prediction of inter dataset similarity by a fleet of autoencoders"):
  with members:
    - "adversarial discriminative domain adaptation"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "emnist an extension of mnist to handwritten letters"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "borrowing treasures from the wealthy deep transfer learning through selective joint fine tuning"
    - "attribute2image conditional image generation from visual attributes"
    - "learning transferable features with deep adaptation networks"
    - "learning to generate images with perceptual similarity metrics"

Connected through hyperedge HE-4 (induced by source paper "dnns as layers of cooperating classifiers"):
  with members:
    - "deep learning using rectified linear units relu"
    - "why do larger models generalize better a theoretical perspective via the xor problem"
    - "exploring generalization in deep learning"
    - "sharp minima can generalize for deep nets"
    - "spectrally normalized margin bounds for neural networks"
    - "opening the black box of deep neural networks via information"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "semi supervised learning method based on predefined evenly distributed class centroids"):
  with members:
    - "a new loss function for cnn classifier based on pre defined evenly distributed class centroids"
    - "a classification supervised auto encoder based on predefined evenly distributed class centroids"
    - "mixmatch a holistic approach to semi supervised learning"
    - "additive margin softmax for face verification"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "mixup beyond empirical risk minimization"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"

Connected through hyperedge HE-6 (induced by source paper "approximation smooth and sparse functions by deep neural networks without saturation"):
  with members:
    - "realizing data features by deep nets"
    - "deep neural networks for rotation invariance approximation and learning"
    - "generalization and expressivity for deep nets"
    - "construction of neural networks for realization of localized deep learning"
    - "universality of deep convolutional neural networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"
    - "constructive neural network learning"

Connected through hyperedge HE-7 (induced by source paper "handwritten optical character recognition ocr a comprehensive systematic literature review slr"):
  with members:
    - "a novel framework for automatic detection of autism a study on corpus callosum and intracranial brain volume"
    - "a novel database of children s spontaneous facial expressions liris cse"
    - "scene text detection and recognition the deep learning era"
    - "deep residual learning for image recognition"
    - "draw a recurrent neural network for image generation"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "machine recognition of hand written characters using neural networks"

Connected through hyperedge HE-8 (induced by source paper "inter and intra domain knowledge transfer for related tasks in deep character recognition"):
  with members:
    - "do better imagenet models transfer better"
    - "what makes imagenet good for transfer learning"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "going deeper with convolutions"

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)