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: "potrojan powerful neural level trojan designs in deep learning models"
  Abstract: With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Neural network or artificial neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language recognition. However, the more we rely on information technology, the more vulnerable we are. That is, malicious NNs could bring huge threat in the so-called coming AI era. In this paper, for the first time in the literature, we propose a novel approach to design and insert powerful neural-level trojans or PoTrojan in pre-trained NN models. Most of the time, PoTrojans remain inactive, not affecting the normal functions of their host NN models. PoTrojans could only be triggered in very rare conditions. Once activated, however, the PoTrojans could cause the host NN models to malfunction, either falsely predicting or classifying, which is a significant threat to human society of the AI era. We would explain the principles of PoTrojans and the easiness of designing and inserting them in pre-trained deep learning models. PoTrojans doesn't modify the existing architecture or parameters of the pre-trained models, without re-training. Hence, the proposed method is very efficient.

Connected through hyperedge HE-1 (induced by source paper "backdoor attacks against transfer learning with pre trained deep learning models"):
  with members:
    - "defending against misclassification attacks in transfer learning"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "is feature selection secure against training data poisoning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "generative poisoning attack method against neural networks"
    - "magnet a two pronged defense against adversarial examples"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "targeted backdoor attacks on deep learning systems using data poisoning"

Connected through hyperedge HE-2 (induced by source paper "the trojai software framework an opensource tool for embedding trojans into deep learning models"):
  with members:
    - "bypassing backdoor detection algorithms in deep learning"
    - "design of intentional backdoors in sequential models"
    - "trojdrl trojan attacks on deep reinforcement learning agents"
    - "making an invisibility cloak real world adversarial attacks on object detectors"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "strip a defence against trojan attacks on deep neural networks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "tbt targeted neural network attack with bit trojan"):
  with members:
    - "terminal brain damage exposing the graceless degradation in deep neural networks under hardware fault attacks"
    - "bit flip attack crushing neural network with progressive bit search"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "hardware trojan attacks on neural networks"
    - "enhancing robustness of machine learning systems via data transformations"
    - "another flip in the wall of rowhammer defenses"

Connected through hyperedge HE-5 (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"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "red attack resource efficient decision based attack for machine learning"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "genattack practical black box attacks with gradient free optimization"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "towards evaluating the robustness of neural networks"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-7 (induced by source paper "a stealthy hardware trojan exploiting the architectural vulnerability of deep learning architectures input interception attack iia"):
  with members:
    - "detecting sybil attacks using proofs of work and location in vanets"
    - "privacy preserving smart parking system using blockchain and private information retrieval"
    - "b ride ride sharing with privacy preservation trust and fair payment atop public blockchain"
    - "privacy preserving and collusion resistant charging coordination schemes for smart grid"
    - "blockchain based firmware update scheme tailored for autonomous vehicles"
    - "blockchain based charging coordination mechanism for smart grid energy storage units"
    - "hardware trojan attacks on neural networks"

Connected through hyperedge HE-8 (induced by source paper "are you tampering with my data"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "laplacian networks bounding indicator function smoothness for neural networks robustness"
    - "deepdiva a highly functional python framework for reproducible experiments"
    - "is feature selection secure against training data poisoning"
    - "is data clustering in adversarial settings secure"
    - "why do deep convolutional networks generalize so poorly to small image transformations"
    - "peernets exploiting peer wisdom against adversarial attacks"
    - "towards an understanding of neural networks in natural image spaces"

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)