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: "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
  Abstract: Deep neural networks are vulnerable to adversarial attacks. Numerous efforts have focused on defenses that either try to patch `holes' in trained models or try to make it difficult or costly to compute adversarial examples exploiting these holes. In our work, we explore a counter-intuitive approach of constructing "adversarial trapdoors. Unlike prior works that try to patch or disguise vulnerable points in the manifold, we intentionally inject `trapdoors,' artificial weaknesses in the manifold that attract optimized perturbation into certain pre-embedded local optima. As a result, the adversarial generation functions naturally gravitate towards our trapdoors, producing adversarial examples that the model owner can recognize through a known neuron activation signature. #R##N#In this paper, we introduce trapdoors and describe an implementation of trapdoors using similar strategies to backdoor/Trojan attacks. We show that by proactively injecting trapdoors into the models (and extracting their neuron activation signature), we can detect adversarial examples generated by the state of the art attacks (Projected Gradient Descent, Optimization based CW, and Elastic Net) with high detection success rate and negligible impact on normal inputs. These results also generalize across multiple classification domains (image recognition, face recognition and traffic sign recognition). We explore different properties of trapdoors, and discuss potential countermeasures (adaptive attacks) and mitigations.

Connected through hyperedge HE-1 (induced by source paper "detection and recovery of adversarial attacks with injected attractors"):
  with members:
    - "invisible backdoor attacks against deep neural networks"
    - "sitatapatra blocking the transfer of adversarial samples"
    - "hidden trigger backdoor attacks"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "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-2 (induced by source paper "towards certifiable adversarial sample detection"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "sitatapatra blocking the transfer of adversarial samples"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "stateful detection of black box adversarial attacks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

Connected through hyperedge HE-3 (induced by source paper "hidden trigger backdoor attacks"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning"
    - "poisoning attacks with generative adversarial nets"
    - "using honeypots to catch adversarial attacks on neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-4 (induced by source paper "universal litmus patterns revealing backdoor attacks in cnns"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "using honeypots to catch adversarial attacks on neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "certified defenses for data poisoning attacks"
    - "attention is all you need"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"

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)