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: "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
  Abstract: A trojan backdoor is a hidden pattern typically implanted in a deep neural network. It could be activated and thus forces that infected model behaving abnormally only when an input data sample with a particular trigger present is fed to that model. As such, given a deep neural network model and clean input samples, it is very challenging to inspect and determine the existence of a trojan backdoor. Recently, researchers design and develop several pioneering solutions to address this acute problem. They demonstrate the proposed techniques have a great potential in trojan detection. However, we show that none of these existing techniques completely address the problem. On the one hand, they mostly work under an unrealistic assumption (e.g. assuming availability of the contaminated training database). On the other hand, the proposed techniques cannot accurately detect the existence of trojan backdoors, nor restore high-fidelity trojan backdoor images, especially when the triggers pertaining to the trojan vary in size, shape and position. In this work, we propose TABOR, a new trojan detection technique. Conceptually, it formalizes a trojan detection task as a non-convex optimization problem, and the detection of a trojan backdoor as the task of resolving the optimization through an objective function. Different from the existing technique also modeling trojan detection as an optimization problem, TABOR designs a new objective function--under the guidance of explainable AI techniques as well as heuristics--that could guide optimization to identify a trojan backdoor in a more effective fashion. In addition, TABOR defines a new metric to measure the quality of a trojan backdoor identified. Using an anomaly detection method, we show the new metric could better facilitate TABOR to identify intentionally injected triggers in an infected model and filter out false alarms......

Connected through hyperedge HE-1 (induced by source paper "februus input purification defense against trojan attacks on deep neural network systems"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "sentinet detecting physical attacks against deep learning systems"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "how to backdoor federated learning"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-2 (induced by source paper "on certifying robustness against backdoor attacks via randomized smoothing"):
  with members:
    - "macer attack free and scalable robust training via maximizing certified radius"
    - "certified robustness of community detection against adversarial structural perturbation via randomized smoothing"
    - "tight certificates of adversarial robustness for randomly smoothed classifiers"
    - "certified adversarial robustness via randomized smoothing"
    - "strip a defence against trojan attacks on deep neural networks"
    - "defending neural backdoors via generative distribution modeling"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "detecting backdoor attacks on deep neural networks by activation clustering"

Connected through hyperedge HE-3 (induced by source paper "refit a unified watermark removal framework for deep learning systems with limited data"):
  with members:
    - "robust watermarking of neural network with exponential weighting"
    - "strip a defence against trojan attacks on deep neural networks"
    - "effectiveness of distillation attack and countermeasure on neural network watermarking"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "spectral signatures in backdoor attacks"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (induced by source paper "dawn dynamic adversarial watermarking of neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "knockoff nets stealing functionality of black box models"
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "prada protecting against dnn model stealing attacks"
    - "defending against machine learning model stealing attacks using deceptive perturbations"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "strip a defence against trojan attacks on deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "model reuse attacks on deep learning systems"
    - "sentinet detecting physical attacks against deep learning systems"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "blackmarks black box multi bit watermarking for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "neuroninspect detecting backdoors in neural networks via output explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "sentinet detecting physical attacks against deep learning systems"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "interpretable convolutional neural networks"
    - "why should i trust you explaining the predictions of any classifier"
    - "deepid3 face recognition with very deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "design and evaluation of a multi domain trojan detection method on deep neural networks"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "model reuse attacks on deep learning systems"
    - "federated learning strategies for improving communication efficiency"
    - "adversarial patch"
    - "sentinet detecting physical attacks against deep learning systems"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-8 (induced by source paper "revealing backdoors post training in dnn classifiers via novel inference on optimized perturbations inducing group misclassification"):
  with members:
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "spectral signatures in backdoor attacks"
    - "when not to classify detection of reverse engineering attacks on dnn image classifiers"
    - "when not to classify anomaly detection of attacks ada on dnn classifiers at test time"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "practical black box attacks against machine learning"

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.CR(Cryptography and Security)