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 "hidden trigger backdoor attacks"):
  Abstract: With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topic. Backdoor attacks are a form of adversarial attacks on deep networks where the attacker provides poisoned data to the victim to train the model with, and then activates the attack by showing a specific small trigger pattern at the test time. Most state-of-the-art backdoor attacks either provide mislabeled poisoning data that is possible to identify by visual inspection, reveal the trigger in the poisoned data, or use noise to hide the trigger. We propose a novel form of backdoor attack where poisoned data look natural with correct labels and also more importantly, the attacker hides the trigger in the poisoned data and keeps the trigger secret until the test time. We perform an extensive study on various image classification settings and show that our attack can fool the model by pasting the trigger at random locations on unseen images although the model performs well on clean data. We also show that our proposed attack cannot be easily defended using a state-of-the-art defense algorithm for backdoor attacks.

Includes members:
  Member "strip a defence against trojan attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "detection and recovery of adversarial attacks with injected attractors")
  Member "a target agnostic attack on deep models exploiting security vulnerabilities of transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving the adversarial robustness of transfer learning via noisy feature distillation")
  Member "poisoning attacks with generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regularisation can mitigate poisoning attacks a novel analysis based on multiobjective bilevel optimisation")
  Member "using honeypots to catch adversarial attacks on neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fawkes protecting personal privacy against unauthorized deep learning models")
  Member "detecting backdoor attacks on deep neural networks by activation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "fine pruning defending against backdooring attacks on deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "backdoor embedding in convolutional neural network models via invisible perturbation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rab provable robustness against backdoor attacks")

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.CV(Computer Vision and Pattern Recognition)