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 radial kernel networks approximating radially symmetric functions with deep networks"
  Abstract: We prove that a particular deep network architecture is more efficient at approximating radially symmetric functions than the best known 2 or 3 layer networks. We use this architecture to approximate Gaussian kernel SVMs, and subsequently improve upon them with further training. The architecture and initial weights of the Deep Radial Kernel Network are completely specified by the SVM and therefore sidesteps the problem of empirically choosing an appropriate deep network architecture.

Connected through hyperedge HE-1 (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"
    - "construction of neural networks for realization of localized deep learning"
    - "universality of deep convolutional neural 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-2 (induced by source paper "realizing data features by deep nets"):
  with members:
    - "generalization and expressivity for deep nets"
    - "construction of neural networks for realization of localized deep learning"
    - "universality of deep convolutional neural 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-3 (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"
    - "construction of neural networks for realization of localized deep learning"
    - "universality of deep convolutional neural 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-4 (induced by source paper "generalization and expressivity for deep nets"):
  with members:
    - "construction of neural networks for realization of localized deep learning"
    - "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"

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)