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: "robust coreset construction for distributed machine learning"
  Abstract: Motivated by the need of solving machine learning problems over distributed datasets, we explore the use of coreset to reduce the communication overhead. Coreset is a summary of the original dataset in the form of a small weighted set in the same sample space. Compared to other data summaries, coreset has the advantage that it can be used as a proxy of the original dataset, potentially for different applications. However, existing coreset construction algorithms are each tailor-made for a specific machine learning problem. Thus, to solve different machine learning problems, one has to collect coresets of different types, defeating the purpose of saving communication overhead. We resolve this dilemma by developing coreset construction algorithms based on k-means/median clustering, that give a provably good approximation for a broad range of machine learning problems with sufficiently continuous cost functions. Through evaluations on diverse datasets and machine learning problems, we verify the robust performance of the proposed algorithms.

Connected through hyperedge HE-1 (induced by source paper "l fgadmm layer wise federated group admm for communication efficient decentralized deep learning"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "accelerating federated learning via momentum gradient descent"
    - "gadmm fast and communication efficient framework for distributed machine learning"
    - "distilling on device intelligence at the network edge"
    - "wireless federated distillation for distributed edge learning with heterogeneous data"
    - "federated learning challenges methods and future directions"
    - "communication efficient distributed learning via lazily aggregated quantized gradients"
    - "scheduling policies for federated learning in wireless networks"

Connected through hyperedge HE-2 (induced by source paper "distilling on device intelligence at the network edge"):
  with members:
    - "gadmm fast and communication efficient framework for distributed machine learning"
    - "federated reinforcement distillation with proxy experience memory"
    - "multi hop federated private data augmentation with sample compression"
    - "federated machine learning concept and applications"
    - "wireless network intelligence at the edge"
    - "large scale distributed neural network training through online distillation"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"

Connected through hyperedge HE-3 (induced by source paper "federated learning for coalition operations"):
  with members:
    - "federated ai for building ai solutions across multiple agencies"
    - "practical secure aggregation for federated learning on user held data"
    - "communication efficient learning of deep networks from decentralized data"

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)