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 nets for local manifold learning"
  Abstract: The problem of extending a function $f$ defined on a training data $\mathcal{C}$ on an unknown manifold $\mathbb{X}$ to the entire manifold and a tubular neighborhood of this manifold is considered in this paper. For $\mathbb{X}$ embedded in a high dimensional ambient Euclidean space $\mathbb{R}^D$, a deep learning algorithm is developed for finding a local coordinate system for the manifold {\bf without eigen--decomposition}, which reduces the problem to the classical problem of function approximation on a low dimensional cube. Deep nets (or multilayered neural networks) are proposed to accomplish this approximation scheme by using the training data. Our methods do not involve such optimization techniques as back--propagation, while assuring optimal (a priori) error bounds on the output in terms of the number of derivatives of the target function. In addition, these methods are universal, in that they do not require a prior knowledge of the smoothness of the target function, but adjust the accuracy of approximation locally and automatically, depending only upon the local smoothness of the target function. Our ideas are easily extended to solve both the pre--image problem and the out--of--sample extension problem, with a priori bounds on the growth of the function thus extended.

Connected through hyperedge HE-1 (induced by source paper "statistical guarantees of generative adversarial networks for distribution estimation"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "improving generalization and stability of generative adversarial networks"
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "improving gan training via binarized representation entropy bre regularization"

Connected through hyperedge HE-2 (induced by source paper "option discovery in the absence of rewards with manifold analysis"):
  with members:
    - "the laplacian in rl learning representations with efficient approximations"
    - "spectral embedding norm looking deep into the spectrum of the graph laplacian"
    - "diversity is all you need learning skills without a reward function"
    - "q learning with nearest neighbors"
    - "eigenoption discovery through the deep successor representation"
    - "curiosity driven exploration by self supervised prediction"
    - "stochastic neural networks for hierarchical reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "nonparametric regression on low dimensional manifolds using deep relu networks"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "universality of deep convolutional neural networks"
    - "squeeze and excitation networks"
    - "recent trends in deep learning based natural language processing"
    - "the expressive power of neural networks a view from the width"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-4 (induced by source paper "efficient approximation of deep relu networks for functions on low dimensional manifolds"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "recent trends in deep learning based natural language processing"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "understanding deep learning requires rethinking generalization"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-5 (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"
    - "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"
    - "distributed learning with regularized least squares"

Connected through hyperedge HE-6 (induced by source paper "approximate newton based statistical inference using only stochastic gradients"):
  with members:
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "starting small learning with adaptive sample sizes"
    - "efficient representation of low dimensional manifolds using deep networks"
    - "stop wasting my gradients practical svrg"
    - "online batch selection for faster training of neural networks"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-7 (induced by source paper "curvature based comparison of two neural networks"):
  with members:
    - "efficient processing of deep neural networks a tutorial and survey"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "curvature aware manifold learning"
    - "efficient representation of low dimensional manifolds using deep networks"
    - "convergent learning do different neural networks learn the same representations"

Connected through hyperedge HE-8 (induced by source paper "dimal deep isometric manifold learning using sparse geodesic sampling"):
  with members:
    - "subspace least squares multidimensional scaling"
    - "efficient representation of low dimensional manifolds using deep networks"
    - "geodesic convolutional neural networks on riemannian manifolds"
    - "adam a method for stochastic optimization"

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)