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: "construction of neural networks for realization of localized deep learning"
  Abstract: The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis and online advertisement, speech and handwriting recognition, computer vision and natural language processing, time series forecasting, and search engines. However, theoretical development of deep learning is still at its infancy. The objective of this paper is to introduce a deep neural network (also called deep-net) approach to localized manifold learning, with each hidden layer endowed with a specific learning task. For the purpose of illustrations, we only focus on deep-nets with three hidden layers, with the first layer for dimensionality reduction, the second layer for bias reduction, and the third layer for variance reduction. A feedback component also designed to eliminate outliers. The main theoretical result in this paper is the order $\mathcal O\left(m^{-2s/(2s+d)}\right)$ of approximation of the regression function with regularity $s$, in terms of the number $m$ of sample points, where the (unknown) manifold dimension $d$ replaces the dimension $D$ of the sampling (Euclidean) space for shallow nets.

Connected through hyperedge HE-1 (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"
    - "universality of deep convolutional neural networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"
    - "constructive neural network learning"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep network approximation for smooth functions"):
  with members:
    - "nonlinear approximation via compositions"
    - "exponential convergence of the deep neural network approximation for analytic functions"
    - "universality of deep convolutional neural networks"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "optimal approximation with sparsely connected deep neural networks"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"

Connected through hyperedge HE-3 (induced by source paper "realization of spatial sparseness by deep relu nets with massive data"):
  with members:
    - "realizing data features by deep nets"
    - "deep neural networks for rotation invariance approximation and learning"
    - "generalization and expressivity for deep nets"
    - "universality of deep convolutional neural networks"
    - "deep radial kernel networks approximating radially symmetric functions with deep networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "distributed learning with regularized least squares"
    - "depth width tradeoffs in approximating natural functions with neural networks"

Connected through hyperedge HE-4 (induced by source paper "function approximation by deep networks"):
  with members:
    - "dimension independent bounds for general shallow networks"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "on the expressive power of overlapping architectures of deep learning"
    - "function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions"
    - "bounding and counting linear regions of deep neural networks"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"

Connected through hyperedge HE-5 (induced by source paper "realizing data features by deep nets"):
  with members:
    - "generalization and expressivity for deep nets"
    - "universality of deep convolutional neural networks"
    - "deep radial kernel networks approximating radially symmetric functions with deep networks"
    - "the power of deeper networks for expressing natural functions"
    - "learning rates for classification with gaussian kernels"
    - "deep vs shallow networks an approximation theory perspective"
    - "learning functions when is deep better than shallow"
    - "depth width tradeoffs in approximating natural functions with neural networks"

Connected through hyperedge HE-6 (induced by source paper "deep neural networks for rotation invariance approximation and learning"):
  with members:
    - "realizing data features by deep nets"
    - "generalization and expressivity for deep nets"
    - "universality of deep convolutional neural networks"
    - "deep radial kernel networks approximating radially symmetric functions with deep networks"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "deep nets for local manifold learning"
    - "distributed learning with regularized least squares"

Connected through hyperedge HE-7 (induced by source paper "generalization and expressivity for deep nets"):
  with members:
    - "deep radial kernel networks approximating radially symmetric functions with deep networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"
    - "constructive neural network learning"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-8 (induced by source paper "enhanced expressive power and fast training of neural networks by random projections"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "the expressive power of neural networks a view from the width"
    - "the power of depth for feedforward neural networks"
    - "iterative hard thresholding for compressed sensing"

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.LG(Machine Learning)