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: "adversarial frontier stitching for remote neural network watermarking"
  Abstract: The state of the art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy processing requirements. Recently, Uchida et al. (2017) proposed to watermark convolutional neural networks by embedding information into their weights. While this is a clear progress towards model protection, this technique solely allows for extracting the watermark from a network that one accesses locally and entirely. This is a clear impediment, as leaked models can be re-used privately, and thus not released publicly for ownership inspection. #R##N#Instead, we aim at allowing the extraction of the watermark from a neural network (or any other machine learning model) that is operated remotely, and available through a service API. To this end, we propose to operate on the model's action itself, tweaking slightly its decision frontiers so that a set of specific queries convey the desired information. #R##N#In present paper, we formally introduce the problem and propose a novel zero-bit watermarking algorithm that makes use of adversarial model examples. While limiting the loss of performance of the protected model, this algorithm allows subsequent extraction of the watermark using only few remote queries. We experiment this approach on the MNIST dataset with three types of neural networks, demonstrating that e.g., watermarking with 100 images incurs a slight accuracy degradation, while being resilient to most removal attacks.

Connected through hyperedge HE-1 (induced by source paper "model watermarking for image processing networks"):
  with members:
    - "stegastamp invisible hyperlinks in physical photographs"
    - "digital watermarking for deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "density aware single image de raining using a multi stream dense network"
    - "hidden hiding data with deep networks"
    - "attention is all you need"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "embedding watermarks into deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep neural network fingerprinting by conferrable adversarial examples"):
  with members:
    - "structure matters towards generating transferable adversarial images"
    - "high fidelity extraction of neural network models"
    - "on the robustness of the backdoor based watermarking in deep neural networks"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "ipguard protecting the intellectual property of deep neural networks via fingerprinting the classification boundary"
    - "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"
    - "dawn dynamic adversarial watermarking of neural networks"
    - "on evaluating adversarial robustness"

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"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "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"

Connected through hyperedge HE-4 (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"

Connected through hyperedge HE-5 (induced by source paper "ipguard protecting the intellectual property of deep neural networks via fingerprinting the classification boundary"):
  with members:
    - "neural network model extraction attacks in edge devices by hearing architectural hints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "digital watermarking for deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "prada protecting against dnn model stealing attacks"
    - "stealing hyperparameters in machine learning"

Connected through hyperedge HE-6 (induced by source paper "effectiveness of distillation attack and countermeasure on neural network watermarking"):
  with members:
    - "deep learning is robust to massive label noise"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "are you tampering with my data"
    - "embedding watermarks into deep neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "fraternal twins unifying attacks on machine learning and digital watermarking"

Connected through hyperedge HE-7 (induced by source paper "tampernn efficient tampering detection of deployed neural nets"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "digital watermarking for deep neural networks"
    - "have you stolen my model evasion attacks against deep neural network watermarking techniques"
    - "sok security and privacy in machine learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "magnet a two pronged defense against adversarial examples"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-8 (induced by source paper "dawn dynamic adversarial watermarking of neural networks"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "tabor a highly accurate approach to inspecting and restoring trojan backdoors in ai systems"
    - "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"

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)