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: "neural networks and rational functions"
  Abstract: Neural networks and rational functions efficiently approximate each other. In more detail, it is shown here that for any ReLU network, there exists a rational function of degree $O(\text{polylog}(1/\epsilon))$ which is $\epsilon$-close, and similarly for any rational function there exists a ReLU network of size $O(\text{polylog}(1/\epsilon))$ which is $\epsilon$-close. By contrast, polynomials need degree $\Omega(\text{poly}(1/\epsilon))$ to approximate even a single ReLU. When converting a ReLU network to a rational function as above, the hidden constants depend exponentially on the number of layers, which is shown to be tight; in other words, a compositional representation can be beneficial even for rational functions.

Connected through hyperedge HE-1 (induced by source paper "pad e activation units end to end learning of flexible activation functions in deep networks"):
  with members:
    - "universal approximation with deep narrow networks"
    - "learning activation functions a new paradigm of understanding neural networks"
    - "learning combinations of activation functions"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "provable robustness of relu networks via maximization of linear regions"
    - "searching for activation functions"
    - "continuously differentiable exponential linear units"

Connected through hyperedge HE-2 (induced by source paper "representation learning with weighted inner product for universal approximation of general similarities"):
  with members:
    - "skip gram word embeddings in hyperbolic space"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "poincar e embeddings for learning hierarchical representations"
    - "error bounds for approximations with deep relu networks"
    - "sliced wasserstein kernels for probability distributions"
    - "a primer on neural network models for natural language processing"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "rational neural networks for approximating jump discontinuities of graph convolution operator"):
  with members:
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "geometric deep learning going beyond euclidean data"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "semi supervised classification with graph convolutional networks"
    - "why deep neural networks for function approximation"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "deep convolutional networks on graph structured data"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"

Connected through hyperedge HE-4 (induced by source paper "representational power of relu networks and polynomial kernels beyond worst case analysis"):
  with members:
    - "depth width tradeoffs in approximating natural functions with neural networks"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"
    - "train faster generalize better stability of stochastic gradient descent"

Connected through hyperedge HE-5 (induced by source paper "negative results for approximation using single layer and multilayer feedforward neural networks"):
  with members:
    - "on the approximation by single hidden layer feedforward neural networks with fixed weights"
    - "benefits of depth in neural networks"

Connected through hyperedge HE-6 (induced by source paper "polynomial regression as an alternative to neural nets"):
  with members:
    - "deep forest towards an alternative to deep neural networks"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-7 (induced by source paper "sum product quotient networks"):
  with members:
    - "on the expressive power of overlapping architectures of deep learning"
    - "inductive bias of deep convolutional networks through pooling geometry"
    - "neural autoregressive distribution estimation"
    - "density estimation using real nvp"
    - "pixel recurrent neural networks"
    - "adam a method for stochastic optimization"
    - "a survey on latent tree models and applications"
    - "sum product networks a new deep architecture"

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)