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 "adversarial frontier stitching for remote neural network watermarking"):
  Abstract: The state of the art performance of deep learning models comes at a high cost for companies and institutions, due to the tedious data collection and the heavy processing requirements. Recently, Uchida et al. (2017) proposed to watermark convolutional neural networks by embedding information into their weights. While this is a clear progress towards model protection, this technique solely allows for extracting the watermark from a network that one accesses locally and entirely. This is a clear impediment, as leaked models can be re-used privately, and thus not released publicly for ownership inspection. #R##N#Instead, we aim at allowing the extraction of the watermark from a neural network (or any other machine learning model) that is operated remotely, and available through a service API. To this end, we propose to operate on the model's action itself, tweaking slightly its decision frontiers so that a set of specific queries convey the desired information. #R##N#In present paper, we formally introduce the problem and propose a novel zero-bit watermarking algorithm that makes use of adversarial model examples. While limiting the loss of performance of the protected model, this algorithm allows subsequent extraction of the watermark using only few remote queries. We experiment this approach on the MNIST dataset with three types of neural networks, demonstrating that e.g., watermarking with 100 images incurs a slight accuracy degradation, while being resilient to most removal attacks.

Includes members:
  Member "tampernn efficient tampering detection of deployed neural nets":
    also connected through other hyperedges:
      (none)
  Member "digital watermarking for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "turning your weakness into a strength watermarking deep neural networks by backdooring":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "stealing neural networks via timing side channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how to 0wn nas in your spare time")
  Member "are adversarial examples inevitable":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "deepsigns a generic watermarking framework for ip protection of deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "survey of attacks and defenses on edge deployed neural networks")
  Member "audio adversarial examples targeted attacks on speech to text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "adversarial perturbations against real time video classification systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "motion excited sampler video adversarial attack with sparked prior")

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)