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: "have you stolen my model evasion attacks against deep neural network watermarking techniques"
  Abstract: Deep neural networks have had enormous impact on various domains of computer science, considerably outperforming previous state of the art machine learning techniques. To achieve this performance, neural networks need large quantities of data and huge computational resources, which heavily increases their construction costs. The increased cost of building a good deep neural network model gives rise to a need for protecting this investment from potential copyright infringements. Legitimate owners of a machine learning model want to be able to reliably track and detect a malicious adversary that tries to steal the intellectual property related to the model. Recently, this problem was tackled by introducing in deep neural networks the concept of watermarking, which allows a legitimate owner to embed some secret information(watermark) in a given model. The watermark allows the legitimate owner to detect copyright infringements of his model. This paper focuses on verifying the robustness and reliability of state-of- the-art deep neural network watermarking schemes. We show that, a malicious adversary, even in scenarios where the watermark is difficult to remove, can still evade the verification by the legitimate owners, thus avoiding the detection of model theft.

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 "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-3 (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-4 (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"
    - "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"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-5 (induced by source paper "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn"):
  with members:
    - "robust watermarking of neural network with exponential weighting"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "embedding watermarks into deep neural networks"
    - "dual path networks"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-6 (induced by source paper "stealing knowledge from protected deep neural networks using composite unlabeled data"):
  with members:
    - "copycat cnn stealing knowledge by persuading confession with random non labeled data"
    - "digital watermarking for deep neural networks"
    - "defending against machine learning model stealing attacks using deceptive perturbations"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"
    - "deepapt nation state apt attribution using end to end deep neural networks"
    - "embedding watermarks into deep neural networks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "adversarial frontier stitching for remote neural network watermarking"

Connected through hyperedge HE-7 (induced by source paper "prediction poisoning utility constrained defenses against model stealing attacks"):
  with members:
    - "knockoff nets stealing functionality of black box models"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "prada protecting against dnn model stealing attacks"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "deep mutual learning"
    - "emnist an extension of mnist to handwritten letters"
    - "stealing machine learning models via prediction apis"

Connected through hyperedge HE-8 (induced by source paper "mimosanet an unrobust neural network preventing model stealing"):
  with members:
    - "robust watermarking of neural network with exponential weighting"
    - "reversible image watermarking for health informatics systems using distortion compensation in wavelet domain"
    - "deepobfuscation securing the structure of convolutional neural networks via knowledge distillation"
    - "adam a method for stochastic optimization"

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)