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 "practical and bilateral privacy preserving federated learning"):
  Abstract: Federated learning, as an emerging distributed training model of neural networks without collecting raw data, has attracted widespread attention. However, almost all existing researches of federated learning only consider protecting the privacy of clients, but not preventing model iterates and final model parameters from leaking to untrusted clients and external attackers. In this paper, we present the first bilateral privacy-preserving federated learning scheme, which protects not only the raw training data of clients, but also model iterates during the training phase as well as final model parameters. Specifically, we present an efficient privacy-preserving technique to mask or encrypt the global model, which not only allows clients to train over the noisy global model, but also ensures only the server can obtain the exact updated model. Detailed security analysis shows that clients can access neither model iterates nor the final global model; meanwhile, the server cannot obtain raw training data of clients from additional information used for recovering the exact updated model. Finally, extensive experiments demonstrate the proposed scheme has comparable model accuracy with traditional federated learning without bringing much extra communication overhead.

Includes members:
  Member "skin lesion analysis toward melanoma detection 2018 a challenge hosted by the international skin imaging collaboration isic":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semi supervised few shot learning for medical image segmentation")
  Member "advances and open problems in federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "trends and advancements in deep neural network communication")
  Member "privacy preserving federated brain tumour segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the future of digital health with federated learning")
  Member "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "real time federated evolutionary neural architecture search")
  Member "semi cyclic stochastic gradient descent":
    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 for healthcare informatics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the future of digital health with federated learning")
  Member "federated learning challenges methods and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trends and advancements in deep neural network communication")
  Member "agnostic federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "scaffold stochastic controlled averaging for 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)