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: "defense against adversarial images using web scale nearest neighbor search"
  Abstract: A plethora of recent work has shown that convolutional networks are not robust to adversarial images: images that are created by perturbing a sample from the data distribution as to maximize the loss on the perturbed example. In this work, we hypothesize that adversarial perturbations move the image away from the image manifold in the sense that there exists no physical process that could have produced the adversarial image. This hypothesis suggests that a successful defense mechanism against adversarial images should aim to project the images back onto the image manifold. We study such defense mechanisms, which approximate the projection onto the unknown image manifold by a nearest-neighbor search against a web-scale image database containing tens of billions of images. Empirical evaluations of this defense strategy on ImageNet suggest that it is very effective in attack settings in which the adversary does not have access to the image database. We also propose two novel attack methods to break nearest-neighbor defenses, and demonstrate conditions under which nearest-neighbor defense fails. We perform a series of ablation experiments, which suggest that there is a trade-off between robustness and accuracy in our defenses, that a large image database (with hundreds of millions of images) is crucial to get good performance, and that careful construction the image database is important to be robust against attacks tailored to circumvent our defenses.

Connected through hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense"):
  with members:
    - "curls whey boosting black box adversarial attacks"
    - "one pixel attack for fooling deep neural networks"
    - "metric learning with horde high order regularizer for deep embeddings"
    - "sparse and imperceivable adversarial attacks"
    - "who s afraid of adversarial queries the impact of image modifications on content based image retrieval"
    - "hopskipjumpattack a query efficient decision based attack"
    - "towards adversarially robust object detection"
    - "adversarial learning with margin based triplet embedding regularization"

Connected through hyperedge HE-2 (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-3 (induced by source paper "minimum norm adversarial examples on knn and knn based models"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "adversarial examples for non parametric methods attacks defenses and large sample limits"
    - "defending against adversarial examples with k nearest neighbor"
    - "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-4 (induced by source paper "defense against adversarial attacks using feature scattering based adversarial training"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "joint adversarial training incorporating both spatial and pixel attacks"
    - "towards adversarially robust object detection"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "adversarial patch"
    - "feature denoising for improving adversarial robustness"
    - "adversarial logit pairing"
    - "adversarial text generation via feature mover s distance"

Connected through hyperedge HE-5 (induced by source paper "detecting adversarial samples using influence functions and nearest neighbors"):
  with members:
    - "defending against adversarial examples with k nearest neighbor"
    - "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-6 (induced by source paper "on the need for topology aware generative models for manifold based defenses"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "stochastic activation pruning for robust adversarial defense"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "monge amp ere flow for generative modeling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "countering adversarial images using input transformations"

Connected through hyperedge HE-7 (induced by source paper "defending against adversarial examples with k nearest neighbor"):
  with members:
    - "adversarial training with voronoi constraints"
    - "sorting out lipschitz function approximation"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "towards the first adversarially robust neural network model on mnist"
    - "l2 nonexpansive neural networks"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-8 (induced by source paper "improving adversarial robustness via attention and adversarial logit pairing"):
  with members:
    - "robust neural networks using randomized adversarial training"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "delta deep learning transfer using feature map with attention for convolutional networks"
    - "dsfd dual shot face detector"
    - "stochastic activation pruning for robust adversarial defense"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial logit pairing"

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.CV(Computer Vision and Pattern Recognition)