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: "the power of deeper networks for expressing natural functions"
  Abstract: It is well-known that neural networks are universal approximators, but that deeper networks tend in practice to be more powerful than shallower ones. We shed light on this by proving that the total number of neurons $m$ required to approximate natural classes of multivariate polynomials of $n$ variables grows only linearly with $n$ for deep neural networks, but grows exponentially when merely a single hidden layer is allowed. We also provide evidence that when the number of hidden layers is increased from $1$ to $k$, the neuron requirement grows exponentially not with $n$ but with $n^{1/k}$, suggesting that the minimum number of layers required for practical expressibility grows only logarithmically with $n$.

Connected through hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning"):
  with members:
    - "learning representations for neural network based classification using the information bottleneck principle"
    - "dynamics aware unsupervised discovery of skills"
    - "causal confusion in imitation learning"
    - "unsupervised discovery of temporal structure in noisy data with dynamical components analysis"
    - "learnability for the information bottleneck"
    - "pareto optimal data compression for binary classification tasks"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "the conditional entropy bottleneck"

Connected through hyperedge HE-2 (induced by source paper "translating diffusion wavelets and regularisation into residual networks"):
  with members:
    - "pde based group equivariant convolutional neural networks"
    - "regularization by architecture a deep prior approach for inverse problems"
    - "analysis of invariance and robustness via invertibility of relu networks"
    - "regularization for deep learning a taxonomy"
    - "stable architectures for deep neural networks"
    - "networks for nonlinear diffusion problems in imaging"
    - "wavelet convolutional neural networks"
    - "forward stability of resnet and its variants"

Connected through hyperedge HE-3 (induced by source paper "duality of width and depth of neural networks"):
  with members:
    - "effect of depth and width on local minima in deep learning"
    - "linear algebra and duality of neural networks"
    - "model compression via distillation and quantization"
    - "universal approximation with quadratic deep networks"
    - "fuzzy logic interpretation of quadratic networks"
    - "toward deeper understanding of neural networks the power of initialization and a dual view on expressivity"
    - "why deep neural networks for function approximation"
    - "xnor net imagenet classification using binary convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "discovering nonlinear relations with minimum predictive information regularization"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "mine mutual information neural estimation"
    - "vain attentional multi agent predictive modeling"
    - "permutation equivariant neural networks applied to dynamics prediction"
    - "interaction networks for learning about objects relations and physics"
    - "a compositional object based approach to learning physical dynamics"
    - "adam a method for stochastic optimization"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-5 (induced by source paper "understanding geometry of encoder decoder cnns"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a differential topological view of challenges in learning with feedforward neural networks"
    - "k space deep learning for accelerated mri"
    - "dnn or k nn that is the generalize vs memorize question"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"

Connected through hyperedge HE-6 (induced by source paper "optimal function approximation with relu neural networks"):
  with members:
    - "universality of deep convolutional neural networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "optimal approximation of continuous functions by very deep relu networks"
    - "expressive power of recurrent neural networks"
    - "the expressive power of neural networks a view from the width"
    - "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-7 (induced by source paper "universal deep beamformer for variable rate ultrasound imaging"):
  with members:
    - "a deep learning framework for single sided sound speed inversion in medical ultrasound"
    - "sparse convolutional beamforming for ultrasound imaging"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "high quality ultrasonic multi line transmission through deep learning"
    - "image reconstruction by domain transform manifold learning"
    - "high frame rate cardiac ultrasound imaging with deep learning"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"

Connected through hyperedge HE-8 (induced by source paper "tangent space separability in feedforward neural networks"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "exploring generalization in deep learning"
    - "comparing information theoretic measures of complexity in boltzmann machines"
    - "sharp minima can generalize for deep nets"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "exploiting linear structure within convolutional networks for efficient evaluation"

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)