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 "age based scheduling policy for federated learning in mobile edge networks"):
  Abstract: Federated learning (FL) is a machine learning model that preserves data privacy in the training process. Specifically, FL brings the model directly to the user equipments (UEs) for local training, where an edge server periodically collects the trained parameters to produce an improved model and sends it back to the UEs. However, since communication usually occurs through a limited spectrum, only a portion of the UEs can update their parameters upon each global aggregation. As such, new scheduling algorithms have to be engineered to facilitate the full implementation of FL. In this paper, based on a metric termed the age of update (AoU), we propose a scheduling policy by jointly accounting for the staleness of the received parameters and the instantaneous channel qualities to improve the running efficiency of FL. The proposed algorithm has low complexity and its effectiveness is demonstrated by Monte Carol simulations.

Includes members:
  Member "federated learning over wireless fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless federated learning with local differential privacy")
  Member "scheduling policies for federated learning in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance analysis and optimization in privacy preserving federated learning")
  Member "client selection for federated learning with heterogeneous resources in mobile edge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tifl a tier based federated learning system")
  Member "the roadmap to 6g ai empowered wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when wireless security meets machine learning motivation challenges and research directions")
  Member "coded federated computing in wireless networks with straggling devices and imperfect csi":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "a joint learning and communications framework for federated learning over wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "federated learning for resource constrained iot devices panoramas and state of the art")
  Member "high dimensional stochastic gradient quantization for communication efficient edge learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an introduction to communication efficient edge machine 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.IT(Information Theory)