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 "bypassing backdoor detection algorithms in deep learning"):
  Abstract: Deep learning models are known to be vulnerable to various adversarial manipulations of the training data, model parameters, and input data. In particular, an adversary can modify the training data and model parameters to embed backdoors into the model, so the model behaves according to the adversary's objective if the input contains the backdoor features (e.g., a stamp on an image). The poisoned model's behavior on clean data, however, remains unchanged. Many detection algorithms are designed to detect backdoors on input samples or model activation functions, in order to remove the backdoor. These algorithms rely on the statistical difference between the latent representations of backdoor-enabled and clean input data in the poisoned model. In this paper, we design an adversarial backdoor embedding algorithm that can bypass the existing detection algorithms including the state-of-the-art techniques (published in IEEE S\&P 2019 and NeurIPS 2018). We design a strategic adversarial training that optimizes the original loss function of the model, and also maximizes the indistinguishability of the hidden representations of poisoned data and clean data. We show the effectiveness of our attack on multiple datasets and model architectures. This work calls for designing adversary-aware defense mechanisms for backdoor detection algorithms.

Includes members:
  Member "a little is enough circumventing defenses for distributed learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards byzantine resilient learning in decentralized systems")
  Member "detecting backdoor attacks on deep neural networks by activation clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "semantic adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "breaking certified defenses semantic adversarial examples with spoofed robustness certificates")
  Member "generalized byzantine tolerant sgd":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to detect malicious clients for robust federated learning")
  Member "byzantine robust distributed learning towards optimal statistical rates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure federated learning")
  Member "spectral signatures in backdoor attacks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "how to backdoor federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trends and advancements in deep neural network communication")

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)