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: "defending against adversarial examples with k nearest neighbor"
  Abstract: Robustness is an increasingly important property of machine learning models as they become more and more prevalent. We propose a defense against adversarial examples based on a k-nearest neighbor (kNN) on the intermediate activation of neural networks. Our scheme surpasses state-of-the-art defenses on MNIST and CIFAR-10 against l2-perturbation by a significant margin. With our models, the mean perturbation norm required to fool our MNIST model is 3.07 and 2.30 on CIFAR-10. Additionally, we propose a simple certifiable lower bound on the l2-norm of the adversarial perturbation using a more specific version of our scheme, a 1-NN on representations learned by a Lipschitz network. Our model provides a nontrivial average lower bound of the perturbation norm, comparable to other schemes on MNIST with similar clean accuracy.

Connected through hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey"):
  with members:
    - "unmask adversarial detection and defense through robust feature alignment"
    - "a research agenda dynamic models to defend against correlated attacks"
    - "analysis of confident classifiers for out of distribution detection"
    - "adversarial attacks on time series"
    - "adversarial attacks on deep neural networks for time series classification"
    - "mimicgan robust projection onto image manifolds with corruption mimicking"
    - "analyzing adversarial attacks against deep learning for intrusion detection in iot networks"
    - "deep learning for anomaly detection a survey"

Connected through hyperedge HE-2 (induced by source paper "minimum norm adversarial examples on knn and knn based models"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "fast rates for a knn classifier robust to unknown asymmetric label noise"
    - "adversarial training with voronoi constraints"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "towards the first adversarially robust neural network model on mnist"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "detecting adversarial samples using influence functions and nearest neighbors"):
  with members:
    - "defense against adversarial images using web scale nearest neighbor search"
    - "on evaluating adversarial robustness"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "dnn or k nn that is the generalize vs memorize question"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-4 (induced by source paper "self supervised adversarial training"):
  with members:
    - "on mutual information maximization for representation learning"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "a kernelized manifold mapping to diminish the effect of adversarial perturbations"
    - "learning representations by maximizing mutual information across views"
    - "unsupervised feature learning via non parametric instance level discrimination"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"
    - "comdefend an efficient image compression model to defend adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "advknn adversarial attacks on k nearest neighbor classifiers with approximate gradients"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "defense against adversarial images using web scale nearest neighbor search"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "harnessing the vulnerability of latent layers in adversarially trained models"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "countering adversarial images using input transformations"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "mask r cnn"

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)