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: "universal approximation with quadratic deep networks"
  Abstract: Recently, deep learning has achieved huge successes in many important applications. In our previous studies, we proposed quadratic/second-order neurons and deep quadratic neural networks. In a quadratic neuron, the inner product of a vector of data and the corresponding weights in a conventional neuron is replaced with a quadratic function. The resultant quadratic neuron enjoys an enhanced expressive capability over the conventional neuron. However, how quadratic neurons improve the expressing capability of a deep quadratic network has not been studied up to now, preferably in relation to that of a conventional neural network. Regarding this, we ask four basic questions in this paper: (1) for the one-hidden-layer network structure, is there any function that a quadratic network can approximate much more efficiently than a conventional network? (2) for the same multi-layer network structure, is there any function that can be expressed by a quadratic network but cannot be expressed with conventional neurons in the same structure? (3) Does a quadratic network give a new insight into universal approximation? (4) To approximate the same class of functions with the same error bound, is a quantized quadratic network able to enjoy a lower number of weights than a quantized conventional network? Our main contributions are the four interconnected theorems shedding light upon these four questions and demonstrating the merits of a quadratic network in terms of expressive efficiency, unique capability, compact architecture and computational capacity respectively.

Connected through hyperedge HE-1 (induced by source paper "no spurious local minima in deep quadratic networks"):
  with members:
    - "on the connection between learning two layers neural networks and tensor decomposition"
    - "polynomial regression as an alternative to neural nets"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "convergence results for neural networks via electrodynamics"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"

Connected through hyperedge HE-2 (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"
    - "fuzzy logic interpretation of quadratic networks"
    - "the power of deeper networks for expressing natural functions"
    - "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-3 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising"):
  with members:
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "fuzzy logic interpretation of artificial neural networks"
    - "dense associative memory is robust to adversarial inputs"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "non linear convolution filters for cnn based learning"
    - "a new type of neurons for machine learning"

Connected through hyperedge HE-4 (induced by source paper "universal approximation by a slim network with sparse shortcut connections"):
  with members:
    - "resnet with one neuron hidden layers is a universal approximator"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "a new type of neurons for machine learning"
    - "the power of deeper networks for expressing natural functions"
    - "the expressive power of neural networks a view from the width"
    - "identity mappings in deep residual networks"
    - "deep vs shallow networks an approximation theory perspective"
    - "densely connected convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "progressive operational perceptron with memory"):
  with members:
    - "heterogeneous multilayer generalized operational perceptron"
    - "data driven neural architecture learning for financial time series forecasting"
    - "progressive learning for systematic design of large neural networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"

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)