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: "fuzzy logic interpretation of artificial neural networks"
  Abstract: Over past several years, deep learning has achieved huge successes in various applications. However, such a data-driven approach is often criticized for lack of interpretability. Recently, we proposed artificial quadratic neural networks consisting of second-order neurons in potentially many layers. In each second-order neuron, a quadratic function is used in the place of the inner product in a traditional neuron, and then undergoes a nonlinear activation. With a single second-order neuron, any fuzzy logic operation, such as XOR, can be implemented. In this sense, any deep network constructed with quadratic neurons can be interpreted as a deep fuzzy logic system. Since traditional neural networks and second-order counterparts can represent each other and fuzzy logic operations are naturally implemented in second-order neural networks, it is plausible to explain how a deep neural network works with a second-order network as the system model. In this paper, we generalize and categorize fuzzy logic operations implementable with individual second-order neurons, and then perform statistical/information theoretic analyses of exemplary quadratic neural networks.

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"
    - "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-2 (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"
    - "dense associative memory is robust to adversarial inputs"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "a new type of neurons for machine learning"
    - "the expressive power of neural networks a view from the width"

Connected through hyperedge HE-3 (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"
    - "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"
    - "a new type of neurons for machine learning"
    - "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"

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)