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 "regularisation can mitigate poisoning attacks a novel analysis based on multiobjective bilevel optimisation"):
  Abstract: Machine Learning (ML) algorithms are vulnerable to poisoning attacks, where a fraction of the training data can be manipulated to deliberately degrade the algorithms' performance. Optimal poisoning attacks, which can be formulated as bilevel optimisation problems, help to assess the robustness of learning algorithms in worst-case scenarios. However, current attacks against algorithms with hyperparameters typically assume that these hyperparameters are constant and thus ignore the effect the attack has on them. In this paper, we show that this approach leads to an overly pessimistic view of the robustness of the learning algorithms tested. We propose a novel optimal attack formulation that considers the effect of the attack on the hyperparameters by modelling the attack as a multiobjective bilevel optimisation problem. We apply this novel attack formulation to ML classifiers using $L_2$ regularisation and show that, in contrast to results previously reported in the literature, $L_2$ regularisation enhances the stability of the learning algorithms and helps to partially mitigate poisoning attacks. Our empirical evaluation on different datasets confirms the limitations of previous poisoning attack strategies, evidences the benefits of using $L_2$ regularisation to dampen the effect of poisoning attacks and shows that the regularisation hyperparameter increases as more malicious data points are injected in the training dataset.

Includes members:
  Member "poisoning attacks with generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hidden trigger backdoor attacks")
  Member "sever a robust meta algorithm for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the effectiveness of mitigating data poisoning attacks with gradient shaping")
  Member "is feature selection secure against training data poisoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "poison frogs targeted clean label poisoning attacks on neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "certified defenses for data poisoning attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "towards poisoning of deep learning algorithms with back gradient optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "automated crowdturfing attacks and defenses in online review systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "text analysis in adversarial settings does deception leave a stylistic trace")

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)