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 "toward an automated auction framework for wireless federated learning services market"):
  Abstract: In traditional machine learning, the central server first collects the data owners' private data together and then trains the model. However, people's concerns about data privacy protection are dramatically increasing. The emerging paradigm of federated learning efficiently builds machine learning models while allowing the private data to be kept at local devices. The success of federated learning requires sufficient data owners to jointly utilize their data, computing and communication resources for model training. In this paper, we propose an auction based market model for incentivizing data owners to participate in federated learning. We design two auction mechanisms for the federated learning platform to maximize the social welfare of the federated learning services market. Specifically, we first design an approximate strategy-proof mechanism which guarantees the truthfulness, individual rationality, and computational efficiency. To improve the social welfare, we develop an automated strategy-proof mechanism based on deep reinforcement learning and graph neural networks. The communication traffic congestion and the unique characteristics of federated learning are particularly considered in the proposed model. Extensive experimental results demonstrate that our proposed auction mechanisms can efficiently maximize the social welfare and provide effective insights and strategies for the platform to organize the federated training.

Includes members:
  Member "incentive design for efficient federated learning in mobile networks a contract theory approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to detect malicious clients for robust federated learning")
  Member "in edge ai intelligentizing mobile edge computing caching and communication by federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy efficient federated edge learning with joint communication and computation design")
  Member "agnostic federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "client selection for federated learning with heterogeneous resources in mobile edge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tifl a tier based federated learning system")
  Member "fair resource allocation in federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "joint service pricing and cooperative relay communication for federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "convergence time optimization for federated learning over wireless networks")
  Member "federated learning with non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trends and advancements in deep neural network communication")
  Member "optimal auctions through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bittensor an intermodel intelligence measure")

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.GT(Computer Science and Game Theory)