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: "deepmarks a digital fingerprinting framework for deep neural networks"
  Abstract: This paper proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning (DL). Remarkable progress has been made in the area of deep learning. Sharing the trained DL models has become a trend that is ubiquitous in various fields ranging from biomedical diagnosis to stock prediction. As the availability and popularity of pre-trained models are increasing, it is critical to protect the Intellectual Property (IP) of the model owner. DeepMarks introduces the first fingerprinting methodology that enables the model owner to embed unique fingerprints within the parameters (weights) of her model and later identify undesired usages of her distributed models. The proposed framework embeds the fingerprints in the Probability Density Function (pdf) of trainable weights by leveraging the extra capacity available in contemporary DL models. DeepMarks is robust against fingerprints collusion as well as network transformation attacks, including model compression and model fine-tuning. Extensive proof-of-concept evaluations on MNIST and CIFAR10 datasets, as well as a wide variety of deep neural networks architectures such as Wide Residual Networks (WRNs) and Convolutional Neural Networks (CNNs), corroborate the effectiveness and robustness of DeepMarks framework.

Connected through hyperedge HE-1 (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-2 (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"
    - "sentinet detecting physical attacks against deep learning systems"

Connected through hyperedge HE-3 (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"
    - "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"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-4 (induced by source paper "effectiveness of distillation attack and countermeasure on neural network watermarking"):
  with members:
    - "deep learning is robust to massive label noise"
    - "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"
    - "adversarial frontier stitching for remote neural network watermarking"
    - "fraternal twins unifying attacks on machine learning and digital watermarking"

Connected through hyperedge HE-5 (induced by source paper "a roadmap toward the resilient internet of things for cyber physical systems"):
  with members:
    - "model based verification and validation of an autonomous vehicle system"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "reachability analysis and safety verification for neural network control systems"
    - "introduction of a hybrid monitor to cyber physical systems"
    - "verification for machine learning autonomy and neural networks survey"
    - "re purposing compact neuronal circuit policies to govern reinforcement learning tasks"
    - "efficient deep learning on multi source private data"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"

Connected through hyperedge HE-6 (induced by source paper "membership encoding for deep learning"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "understanding membership inferences on well generalized learning models"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "knock knock who s there membership inference on aggregate location data"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "embedding watermarks into deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "extended version rethinking deep neural network ownership verification embedding passports to defeat ambiguity attacks"):
  with members:
    - "group normalization"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "a convergence theory for deep learning via over parameterization"
    - "embedding watermarks into deep neural networks"
    - "adversarial frontier stitching for remote neural network watermarking"
    - "understanding deep learning requires rethinking generalization"
    - "compression of neural machine translation models via pruning"

Connected through hyperedge HE-8 (induced by source paper "digital passport a novel technological strategy for intellectual property protection of convolutional neural networks"):
  with members:
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "hidden hiding data with deep networks"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "a robust blind watermarking using convolutional neural network"
    - "embedding watermarks into deep neural networks"
    - "adversarial frontier stitching for remote neural network watermarking"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)