human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust and undetectable white box watermarks for deep neural networks"):
  Abstract: Training deep neural networks (DNN) is expensive in terms of computational power and the amount of necessary labeled training data. Thus, deep learning models constitute business value for data owners. Watermarking of deep neural networks can enable their tracing once released by a data owner. In this paper we define and formalize white-box watermarking algorithms for DNNs, where the data owner needs white-box access to the model to extract the watermark. White-box watermarking algorithms have the advantage that they do not impact the accuracy of the watermarked model. We demonstrate a new property inference attack using a DNN that can detect watermarking by any existing, manually designed algorithms regardless of training dataset and model architecture. We then propose the first white-box DNN watermarking algorithm that is undetectable by the property inference attack. We further extend the capacity and robustness of the watermark. Unlike prior watermarking schemes which restrict the content of watermark message to short binary strings, our new scheme largely increase the capacity and flexibility of the embedded watermark message. Experiments show that our new white-box watermarking algorithm does not impact accuracy, is undetectable and robust against moderate model transformation attacks.

Includes members:
  Member "how to prove your model belongs to you a blind watermark based framework to protect intellectual property of dnn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep neural network fingerprinting by conferrable adversarial examples")
  Member "dawn dynamic adversarial watermarking of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "model extraction attacks against recurrent neural networks")
  Member "exploiting unintended feature leakage in collaborative learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "turning your weakness into a strength watermarking deep neural networks by backdooring":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "prada protecting against dnn model stealing attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nass optimizing secure inference via neural architecture search")
  Member "convolutional neural networks for medical image analysis full training or fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the future of digital health with federated learning")
  Member "towards a visual privacy advisor understanding and predicting privacy risks in images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "shape evasion preventing body shape inference of multi stage approaches")
  Member "embedding watermarks into deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "model watermarking for image processing networks")

Question: Please tell me which class the source paper 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)