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: "dense associative memory is robust to adversarial inputs"
  Abstract: Deep neural networks (DNNs) trained in a supervised way suffer from two known problems. First, the minima of the objective function used in learning correspond to data points (also known as rubbish...

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"
    - "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 "unsupervised learning by competing hidden units"):
  with members:
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "biologically motivated algorithms for propagating local target representations"
    - "conducting credit assignment by aligning local representations"
    - "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination"
    - "dense associative memory for pattern recognition"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "semi supervised learning with ladder networks"
    - "difference target propagation"

Connected through hyperedge HE-3 (induced by source paper "local unsupervised learning for image analysis"):
  with members:
    - "unsupervised learning by competing hidden units"
    - "a correlation game for unsupervised learning yields computational interpretations of hebbian excitation anti hebbian inhibition and synapse elimination"
    - "online representation learning with single and multi layer hebbian networks for image classification"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "adversarial feature learning"
    - "dense associative memory for pattern recognition"
    - "deep residual learning for image recognition"
    - "continuous online sequence learning with an unsupervised neural network model"

Connected through hyperedge HE-4 (induced by source paper "motivating the rules of the game for adversarial example research"):
  with members:
    - "detecting adversarial image examples in deep neural networks with adaptive noise reduction"
    - "machine vs machine minimax optimal defense against adversarial examples"
    - "scaling provable adversarial defenses"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "stochastic activation pruning for robust adversarial defense"
    - "adversarial patch"
    - "bidirectional learning for robust neural networks"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"

Connected through hyperedge HE-5 (induced by source paper "shield fast practical defense and vaccination for deep learning using jpeg compression"):
  with members:
    - "provable limitations of deep learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "robust physical world attacks on deep learning models"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "enhancing robustness of machine learning systems via data transformations"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning using pretrained cnn and associative memory bank"):
  with members:
    - "dynamic routing between capsules"
    - "scatternet hybrid deep learning shdl network for object classification"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "dense associative memory for pattern recognition"
    - "a theoretical analysis of deep neural networks for texture classification"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "simultaneous deep transfer across domains and tasks"

Connected through hyperedge HE-7 (induced by source paper "implicit generative modeling of random noise during training for adversarial robustness"):
  with members:
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "generative adversarial trainer defense to adversarial perturbations with gan"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "vulnerability of deep reinforcement learning to policy induction attacks"

Connected through hyperedge HE-8 (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"
    - "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"

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)