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: "hardware trojan attacks on neural networks"
  Abstract: With the rising popularity of machine learning and the ever increasing demand for computational power, there is a growing need for hardware optimized implementations of neural networks and other machine learning models. As the technology evolves, it is also plausible that machine learning or artificial intelligence will soon become consumer electronic products and military equipment, in the form of well-trained models. Unfortunately, the modern fabless business model of manufacturing hardware, while economic, leads to deficiencies in security through the supply chain. In this paper, we illuminate these security issues by introducing hardware Trojan attacks on neural networks, expanding the current taxonomy of neural network security to incorporate attacks of this nature. To aid in this, we develop a novel framework for inserting malicious hardware Trojans in the implementation of a neural network classifier. We evaluate the capabilities of the adversary in this setting by implementing the attack algorithm on convolutional neural networks while controlling a variety of parameters available to the adversary. Our experimental results show that the proposed algorithm could effectively classify a selected input trigger as a specified class on the MNIST dataset by injecting hardware Trojans into $0.03\%$, on average, of neurons in the 5th hidden layer of arbitrary 7-layer convolutional neural networks, while undetectable under the test data. Finally, we discuss the potential defenses to protect neural networks against hardware Trojan attacks.

Connected through hyperedge HE-1 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems"):
  with members:
    - "on adaptive attacks to adversarial example defenses"
    - "on evaluating adversarial robustness"
    - "model reuse attacks on deep learning systems"
    - "adversarial patch"
    - "yolov3 an incremental improvement"
    - "lavan localized and visible adversarial noise"
    - "anomaly detection using one class neural networks"
    - "review of deep learning"

Connected through hyperedge HE-2 (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"
    - "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"
    - "sentinet detecting physical attacks against deep learning systems"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "how to backdoor federated learning"

Connected through hyperedge HE-3 (induced by source paper "adversarial security attacks and perturbations on machine learning and deep learning methods"):
  with members:
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "designing adversarially resilient classifiers using resilient feature engineering"
    - "security for machine learning based systems attacks and challenges during training and inference"
    - "security matters a survey on adversarial machine learning"
    - "security and privacy issues in deep learning"
    - "deep text classification can be fooled"
    - "mlcapsule guarded offline deployment of machine learning as a service"

Connected through hyperedge HE-4 (induced by source paper "detecting ai trojans using meta neural analysis"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "exploiting unintended feature leakage in collaborative learning"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sentinet detecting physical attacks against deep learning systems"
    - "backdooring convolutional neural networks via targeted weight perturbations"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"

Connected through hyperedge HE-5 (induced by source paper "terminal brain damage exposing the graceless degradation in deep neural networks under hardware fault attacks"):
  with members:
    - "page cache attacks"
    - "scalable methods for 8 bit training of neural networks"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "sever a robust meta algorithm for stochastic optimization"
    - "breaking transferability of adversarial samples with randomness"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "training deep neural networks with 8 bit floating point numbers"
    - "nethammer inducing rowhammer faults through network requests"

Connected through hyperedge HE-6 (induced by source paper "regula sub rosa latent backdoor attacks on deep neural networks"):
  with members:
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "adversarial patch"
    - "adversarial logit pairing"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "hu fu hardware and software collaborative attack framework against neural networks"
    - "spectral signatures in backdoor attacks"
    - "feature squeezing detecting adversarial examples in deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "using honeypots to catch adversarial attacks on neural networks"):
  with members:
    - "defending neural backdoors via generative distribution modeling"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "are adversarial examples inevitable"
    - "why do adversarial attacks transfer explaining transferability of evasion and poisoning attacks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-8 (induced by source paper "bit flip attack crushing neural network with progressive bit search"):
  with members:
    - "defensive quantization when efficiency meets robustness"
    - "deeplaser practical fault attack on deep neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"
    - "deflecting adversarial attacks with pixel deflection"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "another flip in the wall of rowhammer defenses"
    - "defense against adversarial attacks using high level representation guided denoiser"

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)