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: "function approximation with zonal function networks with activation functions analogous to the rectified linear unit functions"
  Abstract: A zonal function (ZF) network on the $q$ dimensional sphere $\mathbb{S}^q$ is a network of the form $\mathbf{x}\mapsto \sum_{k=1}^n a_k\phi(\mathbf{x}\cdot\mathbf{x}_k)$ where $\phi :[-1,1]\to\mathbf{R}$ is the activation function, $\mathbf{x}_k\in\mathbb{S}^q$ are the centers, and $a_k\in\mathbb{R}$. While the approximation properties of such networks are well studied in the context of positive definite activation functions, recent interest in deep and shallow networks motivate the study of activation functions of the form $\phi(t)=|t|$, which are not positive definite. In this paper, we define an appropriate smoothess class and establish approximation properties of such networks for functions in this class. The centers can be chosen independently of the target function, and the coefficients are linear combinations of the training data. The constructions preserve rotational symmetries.

Connected through hyperedge HE-1 (induced by source paper "function approximation by deep networks"):
  with members:
    - "dimension independent bounds for general shallow networks"
    - "construction of neural networks for realization of localized deep learning"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "on the expressive power of overlapping architectures of deep learning"
    - "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-2 (induced by source paper "dimension independent bounds for general shallow networks"):
  with members:
    - "optimal approximation of continuous functions by very deep relu networks"
    - "an analysis of training and generalization errors in shallow and deep networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "breaking the curse of dimensionality with convex neural networks"
    - "eignets for function approximation on manifolds"

Connected through hyperedge HE-3 (induced by source paper "an analysis of training and generalization errors in shallow and deep networks"):
  with members:
    - "theory iiib generalization in deep networks"
    - "exploring generalization in deep learning"
    - "theory of deep learning iii explaining the non overfitting puzzle"
    - "a deep learning approach to diabetic blood glucose prediction"
    - "understanding deep learning requires rethinking generalization"
    - "deep vs shallow networks an approximation theory perspective"
    - "train faster generalize better stability of stochastic gradient descent"
    - "eignets for function approximation on manifolds"

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)