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: "efficient representation of low dimensional manifolds using deep networks"
  Abstract: We consider the ability of deep neural networks to represent data that lies near a low-dimensional manifold in a high-dimensional space. We show that deep networks can efficiently extract the intrinsic, low-dimensional coordinates of such data. We first show that the first two layers of a deep network can exactly embed points lying on a monotonic chain, a special type of piecewise linear manifold, mapping them to a low-dimensional Euclidean space. Remarkably, the network can do this using an almost optimal number of parameters. We also show that this network projects nearby points onto the manifold and then embeds them with little error. We then extend these results to more general manifolds.

Connected through hyperedge HE-1 (induced by source paper "gradient dynamics of shallow univariate relu networks"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "how do infinite width bounded norm networks look in function space"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "gradient descent finds global minima of deep neural networks"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-2 (induced by source paper "intrinsic dimension of data representations in deep neural networks"):
  with members:
    - "mechanisms of dimensionality reduction and decorrelation in deep neural networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "dimensionality driven learning with noisy labels"
    - "characterization of visual object representations in rat primary visual cortex"
    - "curvature based comparison of two neural networks"
    - "emergence of invariance and disentanglement in deep representations"
    - "opening the black box of deep neural networks via information"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-3 (induced by source paper "deep geometric prior for surface reconstruction"):
  with members:
    - "pu net point cloud upsampling network"
    - "pcpnet learning local shape properties from raw point clouds"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "high resolution shape completion using deep neural networks for global structure and local geometry inference"
    - "deep image prior"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "algorithmic regularization in over parameterized matrix recovery"

Connected through hyperedge HE-4 (induced by source paper "construction of neural networks for realization of localized deep learning"):
  with members:
    - "divide and conquer local average regression"
    - "deep vs shallow networks an approximation theory perspective"
    - "benefits of depth in neural networks"
    - "learning functions when is deep better than shallow"
    - "why and when can deep but not shallow networks avoid the curse of dimensionality a review"
    - "the power of depth for feedforward neural networks"
    - "consistency analysis of an empirical minimum error entropy algorithm"
    - "an algorithm for training polynomial networks"

Connected through hyperedge HE-5 (induced by source paper "geometry of deep learning for magnetic resonance fingerprinting"):
  with members:
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "geometric deep learning going beyond euclidean data"
    - "mr fingerprinting deep reconstruction network drone"
    - "adam a method for stochastic optimization"
    - "a compressed sensing framework for magnetic resonance fingerprinting"

Connected through hyperedge HE-6 (induced by source paper "geometry of deep convolutional networks"):
  with members:
    - "tropical geometry of deep neural networks"
    - "understanding deep neural networks with rectified linear units"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "inverting visual representations with convolutional networks"
    - "understanding deep image representations by inverting them"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (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"
    - "deep nets for local manifold learning"
    - "stop wasting my gradients practical svrg"
    - "online batch selection for faster training of neural networks"
    - "a tutorial on conformal prediction"

Connected through hyperedge HE-8 (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"
    - "deep nets for local manifold learning"
    - "convergent learning do different neural networks learn the same representations"

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.NE(Neural and Evolutionary Computing)