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: "generalization and expressivity for deep nets"
  Abstract: Along with the rapid development of deep learning in practice, the theoretical explanations for its success become urgent. Generalization and expressivity are two widely used measurements to quantify theoretical behaviors of deep learning. The expressivity focuses on finding functions expressible by deep nets but cannot be approximated by shallow nets with the similar number of neurons. It usually implies the large capacity. The generalization aims at deriving fast learning rate for deep nets. It usually requires small capacity to reduce the variance. Different from previous studies on deep learning, pursuing either expressivity or generalization, we take both factors into account to explore the theoretical advantages of deep nets. For this purpose, we construct a deep net with two hidden layers possessing excellent expressivity in terms of localized and sparse approximation. Then, utilizing the well known covering number to measure the capacity, we find that deep nets possess excellent expressive power (measured by localized and sparse approximation) without enlarging the capacity of shallow nets. As a consequence, we derive near optimal learning rates for implementing empirical risk minimization (ERM) on the constructed deep nets. These results theoretically exhibit the advantage of deep nets from learning theory viewpoints.

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"
    - "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"
    - "the power of depth for feedforward neural networks"

Connected through hyperedge HE-2 (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"
    - "construction of neural networks for realization of localized deep learning"
    - "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-3 (induced by source paper "realizing data features by deep nets"):
  with members:
    - "construction of neural networks for realization of localized deep learning"
    - "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-4 (induced by source paper "deep neural networks for rotation invariance approximation and learning"):
  with members:
    - "realizing data features by deep nets"
    - "construction of neural networks for realization of localized deep learning"
    - "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"

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)