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: "a new type of neurons for machine learning"
  Abstract: In machine learning, the use of an artificial neural network is the mainstream approach. Such a network consists of layers of neurons. These neurons are of the same type characterized by the two features: (1) an inner product of an input vector and a matching weighting vector of trainable parameters and (2) a nonlinear excitation function. Here we investigate the possibility of replacing the inner product with a quadratic function of the input vector, thereby upgrading the 1st order neuron to the 2nd order neuron, empowering individual neurons, and facilitating the optimization of neural networks. Also, numerical examples are provided to illustrate the feasibility and merits of the 2nd order neurons. Finally, further topics are discussed.

Connected through hyperedge HE-1 (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"
    - "universal approximation with quadratic deep networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "fuzzy logic interpretation of artificial neural networks"):
  with members:
    - "geometric understanding of deep learning"
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "interpreting deep visual representations via network dissection"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "a geometric view of optimal transportation and generative model"
    - "visualizing the loss landscape of neural nets"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-3 (induced by source paper "fuzzy logic interpretation of quadratic networks"):
  with members:
    - "geometric understanding of deep learning"
    - "structurally sensitive multi scale deep neural network for low dose ct denoising"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "interpreting deep visual representations via network dissection"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "sharp minima can generalize for deep nets"
    - "the loss surface of deep and wide neural networks"

Connected through hyperedge HE-4 (induced by source paper "universal approximation with quadratic deep networks"):
  with members:
    - "quadratic autoencoder q ae for low dose ct denoising"
    - "on the universal approximability and complexity bounds of quantized relu neural networks"
    - "efficient sparse winograd convolutional neural networks"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "fuzzy logic interpretation of artificial neural networks"
    - "dense associative memory is robust to adversarial inputs"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "the expressive power of neural networks a view from the width"

Connected through hyperedge HE-5 (induced by source paper "universal approximation by a slim network with sparse shortcut connections"):
  with members:
    - "universal approximation with quadratic deep networks"
    - "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"
    - "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-6 (induced by source paper "soft autoencoder and its wavelet shrinkage interpretation"):
  with members:
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "fuzzy logic interpretation of artificial neural networks"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "fastmri an open dataset and benchmarks for accelerated mri"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "a deep convolutional auto encoder with pooling unpooling layers in caffe"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"

Connected through hyperedge HE-7 (induced by source paper "learning from pseudo randomness with an artificial neural network does god play pseudo dice"):
  with members:
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"

Connected through hyperedge HE-8 (induced by source paper "general backpropagation algorithm for training second order neural networks"):
  with members:
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep learning in neural networks"

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)