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 "robust federated training via collaborative machine teaching using trusted instances"):
  Abstract: Federated learning performs distributed model training using local data hosted by agents. It shares only model parameter updates for iterative aggregation at the server. Although it is privacy-preserving by design, federated learning is vulnerable to noise corruption of local agents, as demonstrated in the previous study on adversarial data poisoning threat against federated learning systems. Even a single noise-corrupted agent can bias the model training. In our work, we propose a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers, to improve robustness of the federated training process against local data corruption. We assume that each local agent (teacher) have the resources to verify a small portions of trusted instances, which may not by itself be adequate for learning. In the proposed collaborative machine teaching method, these trusted instances guide the distributed agents to jointly select a compact while informative training subset from data hosted by their own. Simultaneously, the agents learn to add changes of limited magnitudes into the selected data instances, in order to improve the testing performances of the federally trained model despite of the training data corruption. Experiments on toy and real data demonstrate that our approach can identify training set bugs effectively and suggest appropriate changes to the labels. Our algorithm is a step toward trustworthy machine learning.

Includes members:
  Member "understanding the role of adaptivity in machine teaching the case of version space learners":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive teaching of temporal logic formulas to learners with preferences")
  Member "analyzing federated learning through an adversarial lens":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts")
  Member "teaching inverse reinforcement learners via features and demonstrations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to switch between machines and humans")
  Member "training set debugging using trusted items":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "weakly supervised learning meets ride sharing user experience enhancement")
  Member "the teaching dimension of linear learners":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learner aware teaching inverse reinforcement learning with preferences and constraints")
  Member "machine teaching for bayesian learners in the exponential family":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dataset distillation")
  Member "identifying mislabeled training data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improving training on noisy stuctured labels")

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)