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: "sentinet detecting physical attacks against deep learning systems"
  Abstract: SentiNet is a novel detection framework for physical attacks on neural networks, a class of attacks that constrains an adversarial region to a visible portion of an image. Physical attacks have been shown to be robust and flexible techniques suited for deployment in real-world scenarios. Unlike most other adversarial detection works, SentiNet does not require training a model or preknowledge of an attack prior to detection. This attack-agnostic approach is appealing due to the large number of possible mechanisms and vectors of attack an attack-specific defense would have to consider. By leveraging the neural network's susceptibility to attacks and by using techniques from model interpretability and object detection as detection mechanisms, SentiNet turns a weakness of a model into a strength. We demonstrate the effectiveness of SentiNet on three different attacks - i.e., adversarial examples, data poisoning attacks, and trojaned networks - that have large variations in deployment mechanisms, and show that our defense is able to achieve very competitive performance metrics for all three threats, even against strong adaptive adversaries with full knowledge of SentiNet.

Connected through hyperedge HE-1 (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-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"
    - "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-3 (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"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"

Connected through hyperedge HE-4 (induced by source paper "detecting patch adversarial attacks with image residuals"):
  with members:
    - "de randomized smoothing for certifiable defense against patch attacks"
    - "certified defenses for adversarial patches"
    - "one pixel attack for fooling deep neural networks"
    - "a fourier perspective on model robustness in computer vision"
    - "adversarial patch"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "lavan localized and visible adversarial noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "adversarial perceptual ad blocking meets adversarial machine learning"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "stateful detection of black box adversarial attacks"
    - "percival making in browser perceptual ad blocking practical with deep learning"
    - "who filters the filters understanding the growth usefulness and efficiency of crowdsourced ad blocking"
    - "yolov3 an incremental improvement"
    - "fooling ocr systems with adversarial text images"
    - "adversarially robust generalization requires more data"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-6 (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"
    - "backdooring convolutional neural networks via targeted weight perturbations"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "speech commands a dataset for limited vocabulary speech recognition"

Connected through hyperedge HE-7 (induced by source paper "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"):
  with members:
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "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"
    - "explaining deep learning models a bayesian non parametric approach"
    - "textbugger generating adversarial text against real world applications"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "spectral signatures in backdoor attacks"

Connected through hyperedge HE-8 (induced by source paper "on the robustness of the backdoor based watermarking in deep neural networks"):
  with members:
    - "robust watermarking of neural network with exponential weighting"
    - "strip a defence against trojan attacks on deep neural networks"
    - "deepstego protecting intellectual property of deep neural networks by steganography"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "knockoff nets stealing functionality of black box models"
    - "performance comparison of contemporary dnn watermarking techniques"
    - "deepmarks a digital fingerprinting framework for deep neural networks"

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)