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 "when attackers meet ai learning empowered attacks in cooperative spectrum sensing"):
  Abstract: Defense strategies have been well studied to combat Byzantine attacks that aim to disrupt cooperative spectrum sensing by sending falsified sensing data. However, existing studies usually make network or attack assumptions biased towards the defense (e.g., assuming the prior knowledge of attacks is known). In practice, attackers can adopt any arbitrary behavior and avoid any pre-assumed pattern or assumption used by defense strategies. In this paper, we revisit this traditional security problem and propose a novel learning-empowered framework named Learn-Evaluate-Beat (LEB) to mislead the fusion center. Based on the black-box nature of the fusion center in cooperative spectrum sensing process, our new perspective is to make the adversarial use of machine learning to construct a surrogate model of the fusion center's decision model. Then, we propose a generic algorithm to create malicious sensing data. Our real-world experiments show that the LEB attack is very effective to beat a wide range of existing defense strategies with an up to 82% of success ratio. Given the gap between the new LEB attack and existing defenses, we introduce a non-invasive and parallel method named as influence-limiting policy sided with existing defenses to defend against the LEB-based or other similar attacks, which demonstrates a strong performance in terms of overall disruption ratio reduction by up to 80% of the LEB attacks.

Includes members:
  Member "adversarial risk and the dangers of evaluating against weak attacks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")
  Member "adversarial attacks and defences competition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nesterov accelerated gradient and scale invariance for adversarial attacks")
  Member "sok security and privacy in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "adversarial examples are not easily detected bypassing ten detection methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "learning universal adversarial perturbations with generative models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blind adversarial network perturbations")
  Member "technical report on the cleverhans v2 1 0 adversarial examples library":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "practical black box attacks against machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adversarial ranking attack and defense")
  Member "transferability in machine learning from phenomena to black box attacks using adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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.NI(Networking and Internet Architecture)