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: "characterizing adversarial subspaces using local intrinsic dimensionality"
  Abstract: Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction. To better understand such attacks, a characterization is needed of the properties of regions (the so-called `adversarial subspaces') in which adversarial examples lie. In particular, effective measures are required to discriminate adversarial examples from normal examples in such regions. We tackle this challenge by characterizing the dimensional properties of adversarial regions, via the use of Local Intrinsic Dimensionality (LID). LID assesses the space-filling capability of the region surrounding a reference example, based on the distance distribution of the example to its neighbors. We first provide explanations about how adversarial perturbation can affect the LID characteristic of adversarial regions, and then show empirically that LID characteristics can facilitate the detection of adversarial examples generated using the state-of-the-art attacks. We show that when applied for adversarial detection, an LID-based method can outperform several state-of-the-art detection measures by large margins for five attack strategies across three benchmark datasets. Our analysis of the LID characteristic for adversarial regions not only motivates new directions of effective adversarial defense, but also opens up more challenges for developing new attacks to better understand the vulnerabilities of DNNs.

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 "weighted average precision adversarial example detection in the visual perception of autonomous vehicles"):
  with members:
    - "enhancing cross task transferability of adversarial examples with dispersion reduction"
    - "autonomous human activity classification from ego vision camera and accelerometer data"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "scaling provable adversarial defenses"
    - "stochastic activation pruning for robust adversarial defense"
    - "autonomously and simultaneously refining deep neural network parameters by a bi generative adversarial network aided genetic algorithm"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-3 (induced by source paper "detection and recovery of adversarial attacks with injected attractors"):
  with members:
    - "invisible backdoor attacks against deep neural networks"
    - "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
    - "sitatapatra blocking the transfer of adversarial samples"
    - "hidden trigger backdoor attacks"
    - "feature denoising for improving adversarial robustness"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "deepobfuscation securing the structure of convolutional neural networks via knowledge distillation"

Connected through hyperedge HE-4 (induced by source paper "rab provable robustness against backdoor attacks"):
  with members:
    - "dynamic backdoor attacks against machine learning models"
    - "backdoor attacks against transfer learning with pre trained deep learning models"
    - "provable robust learning based on transformation specific smoothing"
    - "on certifying robustness against backdoor attacks via randomized smoothing"
    - "certified robustness to label flipping attacks via randomized smoothing"
    - "certified adversarial robustness via randomized smoothing"
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-5 (induced by source paper "visionguard runtime detection of adversarial inputs to perception systems"):
  with members:
    - "detection based defense against adversarial examples from the steganalysis point of view"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "lavan localized and visible adversarial noise"
    - "shield fast practical defense and vaccination for deep learning using jpeg compression"
    - "on calibration of modern neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-6 (induced by source paper "representation quality explains adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "dynamic routing between capsules"
    - "countering adversarial images using input transformations"
    - "pixeldefend leveraging generative models to understand and defend against adversarial examples"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"
    - "a study of the effect of jpg compression on adversarial images"

Connected through hyperedge HE-7 (induced by source paper "provable robust learning based on transformation specific smoothing"):
  with members:
    - "breaking certified defenses semantic adversarial examples with spoofed robustness certificates"
    - "certified adversarial robustness via randomized smoothing"
    - "exploiting excessive invariance caused by norm bounded adversarial robustness"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "scaling provable adversarial defenses"
    - "spatially transformed adversarial examples"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-8 (induced by source paper "robustness from simple classifiers"):
  with members:
    - "improving adversarial robustness via promoting ensemble diversity"
    - "theoretically principled trade off between robustness and accuracy"
    - "feature distillation dnn oriented jpeg compression against adversarial examples"
    - "max margin adversarial mma training direct input space margin maximization through adversarial training"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "towards robust detection of adversarial examples"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"

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)