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 "persistent and unforgeable watermarks for deep neural networks"):
  Abstract: As deep learning classifiers continue to mature, model providers with sufficient data and computation resources are exploring approaches to monetize the development of increasingly powerful models. Licensing models is a promising approach, but requires a robust tool for owners to claim ownership of models, i.e. a watermark. Unfortunately, current watermarks are all vulnerable to piracy attacks, where attackers embed forged watermarks into a model to dispute ownership. We believe properties of persistence and piracy resistance are critical to watermarks, but are fundamentally at odds with the current way models are trained and tuned. In this work, we propose two new training techniques (out-of-bound values and null-embedding) that provide persistence and limit the training of certain inputs into trained models. We then introduce "wonder filters", a new primitive that embeds a persistent bit-sequence into a model, but only at initial training time. Wonder filters enable model owners to embed a bit-sequence generated from their private keys into a model at training time. Attackers cannot remove wonder filters via tuning, and cannot add their own filters to pretrained models. We provide analytical proofs of key properties, and experimentally validate them over a variety of tasks and models. Finally, we explore a number of adaptive counter-measures, and show our watermark remains robust.

Includes members:
  Member "rethinking deep neural network ownership verification embedding passports to defeat ambiguity attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "piracy resistant watermarks for deep neural networks")
  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 "sentinet detecting physical attacks against deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rab provable robustness against backdoor attacks")
  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 "robust physical world attacks on deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "facenet a unified embedding for face recognition and clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discrimination aware network pruning for deep model compression")

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)