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 "a crowdsourcing framework for on device federated learning"):
  Abstract: Federated learning (FL) rests on the notion of training a global model in a decentralized manner. Under this setting, mobile devices perform computations on their local data before uploading the required updates to improve the global model. However, when the participating clients implement an uncoordinated computation strategy, the difficulty is to handle the communication efficiency (i.e., the number of communications per iteration) while exchanging the model parameters during aggregation. Therefore, a key challenge in FL is how users participate to build a high-quality global model with communication efficiency. We tackle this issue by formulating a utility maximization problem, and propose a novel crowdsourcing framework to leverage FL that considers the communication efficiency during parameters exchange. First, we show an incentive-based interaction between the crowdsourcing platform and the participating client's independent strategies for training a global learning model, where each side maximizes its own benefit. We formulate a two-stage Stackelberg game to analyze such scenario and find the game's equilibria. Second, we formalize an admission control scheme for participating clients to ensure a level of local accuracy. Simulated results demonstrate the efficacy of our proposed solution with up to 22% gain in the offered reward.

Includes members:
  Member "distributed federated learning for ultra reliable low latency vehicular communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning")
  Member "federated learning over wireless networks convergence analysis and resource allocation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "update aware device scheduling for federated learning at the wireless edge")
  Member "on device federated learning via blockchain and its latency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "trends and advancements in deep neural network communication")
  Member "online job scheduling in distributed machine learning clusters":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication contention aware scheduling of multiple deep learning training jobs")
  Member "federated optimization for heterogeneous networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the future of digital health with federated learning")
  Member "adaptive communication strategies to achieve the best error runtime trade off in local update sgd":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach")
  Member "beyond inferring class representatives user level privacy leakage from federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")

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)