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 "learning to detect malicious clients for robust federated learning"):
  Abstract: Federated learning systems are vulnerable to attacks from malicious clients. As the central server in the system cannot govern the behaviors of the clients, a rogue client may initiate an attack by sending malicious model updates to the server, so as to degrade the learning performance or enforce targeted model poisoning attacks (a.k.a. backdoor attacks). Therefore, timely detecting these malicious model updates and the underlying attackers becomes critically important. In this work, we propose a new framework for robust federated learning where the central server learns to detect and remove the malicious model updates using a powerful detection model, leading to targeted defense. We evaluate our solution in both image classification and sentiment analysis tasks with a variety of machine learning models. Experimental results show that our solution ensures robust federated learning that is resilient to both the Byzantine attacks and the targeted model poisoning attacks.

Includes members:
  Member "a survey on federated learning systems vision hype and reality for data privacy and protection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ternary compression for communication efficient federated learning")
  Member "incentive design for efficient federated learning in mobile networks a contract theory approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mechanism design for multi party machine learning")
  Member "can you really backdoor federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure federated learning")
  Member "semi cyclic stochastic gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure federated learning")
  Member "local model poisoning attacks to byzantine robust federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure federated learning")
  Member "federated learning challenges methods and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trends and advancements in deep neural network communication")
  Member "federated variance reduced stochastic gradient descent with robustness to byzantine attacks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "federated learning of a mixture of global and local models")
  Member "federated machine learning concept and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the future of digital health with federated learning")

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)