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 "on the robustness of the backdoor based watermarking in deep neural networks"):
  Abstract: Obtaining the state of the art performance of deep learning models imposes a high cost to model generators, due to the tedious data preparation and the substantial processing requirements. To protect the model from unauthorized re-distribution, watermarking approaches have been introduced in the past couple of years. The watermark allows the legitimate owner to detect copyright violations of their model. We investigate the robustness and reliability of state-of-the-art deep neural network watermarking schemes. We focus on backdoor-based watermarking and show that an adversary can remove the watermark fully by just relying on public data and without any access to the model's sensitive information such as the training data set, the trigger set or the model parameters. We as well prove the security inadequacy of the backdoor-based watermarking in keeping the watermark hidden by proposing an attack that detects whether a model contains a watermark.

Includes members:
  Member "robust watermarking of neural network with exponential weighting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refit a unified watermark removal framework for deep learning systems with limited data")
  Member "strip a defence against trojan attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "deepstego protecting intellectual property of deep neural networks by steganography":
    also connected through other hyperedges:
      (none)
  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-3 (induced by source paper "deep neural network fingerprinting by conferrable adversarial examples")
  Member "fine pruning defending against backdooring attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "knockoff nets stealing functionality of black box models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "performance comparison of contemporary dnn watermarking techniques":
    also connected through other hyperedges:
      (none)
  Member "deepmarks a digital fingerprinting framework for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep neural network fingerprinting by conferrable adversarial examples")

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.LG(Machine Learning)