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: "variational federated multi task learning"
  Abstract: In classical federated learning a central server coordinates the training of a single model on a massively distributed network of devices. This setting can be naturally extended to a multi-task learning framework, to handle real-world federated datasets that typically show strong non-IID data distributions among devices. Even though federated multi-task learning has been shown to be an effective paradigm for real world datasets, it has been applied only to convex models. In this work we introduce VIRTUAL, an algorithm for federated multi-task learning with non-convex models. In VIRTUAL the federated network of the server and the clients is treated as a star-shaped Bayesian network, and learning is performed on the network using approximated variational inference. We show that this method is effective on real-world federated datasets, outperforming the current state-of-the-art for federated learning.

Connected through hyperedge HE-1 (induced by source paper "federated learning of a mixture of global and local models"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "sgd general analysis and improved rates"
    - "adaptive gradient based meta learning methods"
    - "advances and open problems in federated learning"
    - "tighter theory for local sgd on identical and heterogeneous data"
    - "variance reduced local sgd with lower communication complexity"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "semi cyclic stochastic gradient descent"

Connected through hyperedge HE-2 (induced by source paper "dynamic federated learning"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "asynchronous federated optimization"
    - "agnostic federated learning"
    - "client edge cloud hierarchical federated learning"
    - "first analysis of local gd on heterogeneous data"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-3 (induced by source paper "clustered federated learning model agnostic distributed multi task optimization under privacy constraints"):
  with members:
    - "deepcabac a universal compression algorithm for deep neural networks"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "robust federated learning in a heterogeneous environment"
    - "towards federated learning at scale system design"
    - "federated learning challenges methods and future directions"
    - "robust and communication efficient federated learning from non iid data"
    - "federated machine learning concept and applications"
    - "decentralized deep learning with arbitrary communication compression"

Connected through hyperedge HE-4 (induced by source paper "fedmd heterogenous federated learning via model distillation"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "towards federated learning at scale system design"
    - "incentive design for efficient federated learning in mobile networks a contract theory approach"
    - "semi cyclic stochastic gradient descent"
    - "federated learning challenges methods and future directions"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated optimization for heterogeneous networks"
    - "leaf a benchmark for federated settings"

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)