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 "abnormal client behavior detection in federated learning"):
  Abstract: In federated learning systems, clients are autonomous in that their behaviors are not fully governed by the server. Consequently, a client may intentionally or unintentionally deviate from the prescribed course of federated model training, resulting in abnormal behaviors, such as turning into a malicious attacker or a malfunctioning client. Timely detecting those anomalous clients is therefore critical to minimize their adverse impacts. In this work, we propose to detect anomalous clients at the server side. In particular, we generate low-dimensional surrogates of model weight vectors and use them to perform anomaly detection. We evaluate our solution through experiments on image classification model training over the FEMNIST dataset. Experimental results show that the proposed detection-based approach significantly outperforms the conventional defense-based methods.

Includes members:
  Member "privacy preserving deep learning via weight transmission":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a federated learning framework for privacy preserving and parallel training")
  Member "towards federated learning at scale system design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "incentive design for efficient federated learning in mobile networks a contract theory approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to detect malicious clients for robust federated learning")
  Member "federated machine learning concept and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the future of digital health with federated learning")
  Member "distributed training with heterogeneous data bridging median and mean based algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "salvaging federated learning by local adaptation")
  Member "analyzing federated learning through an adversarial lens":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts")
  Member "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to detect malicious clients for robust federated learning")
  Member "federated learning strategies for improving communication efficiency":
    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)