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: "rethinking deep neural network ownership verification embedding passports to defeat ambiguity attacks"
  Abstract: With the rapid development of deep neural networks (DNN), there emerges an urgent need to protect the trained DNN models from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners. Following recent progresses along this line, we investigate a number of watermark-based DNN ownership verification methods in the face of ambiguity attacks, which aim to cast doubts on ownership verification by forging counterfeit watermarks. It is shown that ambiguity attacks pose serious challenges to existing DNN watermarking methods. As remedies to the above-mentioned loophole, this paper proposes novel passport-based DNN ownership verification schemes which are both robust to network modifications and resilient to ambiguity attacks. The gist of embedding digital passports is to design and train DNN models in a way such that, the DNN model performance of an original task will be significantly deteriorated due to forged passports. In other words genuine passports are not only verified by looking for predefined signatures, but also reasserted by the unyielding DNN model performances. Extensive experimental results justify the effectiveness of the proposed passport-based DNN ownership verification schemes. Code and models are available at this https URL

Connected through hyperedge HE-1 (induced by source paper "piracy resistant watermarks for deep neural networks"):
  with members:
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "stealing hyperparameters in machine learning"
    - "robust physical world attacks on deep learning models"
    - "embedding watermarks into deep neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "a novel method for identifying the deep neural network model with the serial number"):
  with members:
    - "bringing giant neural networks down to earth with unlabeled data"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "digital watermarking for deep neural networks"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "embedding watermarks into deep neural networks"
    - "pruning convolutional neural networks for resource efficient inference"
    - "deep residual learning for image recognition"
    - "net2net accelerating learning via knowledge transfer"

Connected through hyperedge HE-3 (induced by source paper "persistent and unforgeable watermarks for deep neural networks"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "sentinet detecting physical attacks against deep learning systems"
    - "turning your weakness into a strength watermarking deep neural networks by backdooring"
    - "robust physical world attacks on deep learning models"
    - "facenet a unified embedding for face recognition and clustering"

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)