human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "model pruning enables efficient federated learning on edge devices"
  Abstract: Federated learning is a recent approach for distributed model training without sharing the raw data of clients. It allows model training using the large amount of user data collected by edge and mobile devices, while preserving data privacy. A challenge in federated learning is that the devices usually have much lower computational power and communication bandwidth than machines in data centers. Training large-sized deep neural networks in such a federated setting can consume a large amount of time and resources. To overcome this challenge, we propose a method that integrates model pruning with federated learning in this paper, which includes initial model pruning at the server, further model pruning as part of the federated learning process, followed by the regular federated learning procedure. Our proposed approach can save the computation, communication, and storage costs compared to standard federated learning approaches. Extensive experiments on real edge devices validate the benefit of our proposed method.

Connected through hyperedge HE-1 (induced by source paper "adaptive gradient sparsification for efficient federated learning an online learning approach"):
  with members:
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "advances and open problems in federated learning"
    - "introduction to online convex optimization"
    - "federated learning challenges methods and future directions"
    - "layer wise adaptive gradient sparsification for distributed deep learning with convergence guarantees"
    - "federated machine learning concept and applications"
    - "wireless network intelligence at the edge"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-2 (induced by source paper "federated learning for resource constrained iot devices panoramas and state of the art"):
  with members:
    - "elfish resource aware federated learning on heterogeneous edge devices"
    - "one shot federated learning"
    - "privacy is what we care about experimental investigation of federated learning on edge devices"
    - "towards federated learning at scale system design"
    - "compressing rnns for iot devices by 15 38x using kronecker products"
    - "distilling on device intelligence at the network edge"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"

Question: Please tell me which class the center node 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)