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 "hierarchical federated learning across heterogeneous cellular networks"):
  Abstract: We study collaborative machine learning (ML) across wireless devices, each with its own local dataset. Offloading these datasets to a cloud or an edge server to implement powerful ML solutions is often not feasible due to latency, bandwidth and privacy constraints. Instead, we consider federated edge learning (FEEL), where the devices share local updates on the model parameters rather than their datasets. We consider a heterogeneous cellular network (HCN), where small cell base stations (SBSs) orchestrate FL among the mobile users (MUs) within their cells, and periodically exchange model updates with the macro base station (MBS) for global consensus. We employ gradient sparsification and periodic averaging to increase the communication efficiency of this hierarchical federated learning (FL) framework. We then show using CIFAR-10 dataset that the proposed hierarchical learning solution can significantly reduce the communication latency without sacrificing the model accuracy.

Includes members:
  Member "machine learning at the wireless edge distributed stochastic gradient descent over the air":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless federated learning with local differential privacy")
  Member "collaborative machine learning at the wireless edge with blind transmitters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "one bit over the air aggregation for communication efficient federated edge learning design and convergence analysis")
  Member "accelerating dnn training in wireless federated edge learning system":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "convergence time optimization for federated learning over wireless networks")
  Member "towards federated learning at scale system design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "wireless federated distillation for distributed edge learning with heterogeneous data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "personalized federated learning for intelligent iot applications a cloud edge based framework")
  Member "robust and communication efficient federated learning from non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ternary compression for communication efficient federated learning")
  Member "a distributed hierarchical sgd algorithm with sparse global reduction":
    also connected through other hyperedges:
      (none)
  Member "edge assisted hierarchical federated learning with non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure 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)