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: "mass personalization of deep learning"
  Abstract: We discuss training techniques, objectives and metrics toward mass personalization of deep learning models. In machine learning, personalization refers to the fact that every trained model should be targeted towards an individual by optimizing one or several performance metrics and often obeying additional constraints. We investigate three methods for personalized training of neural networks. They constitute three forms of curriculum learning. The methods are partially inspired by the "shaping" concept from psychology. Interestingly, we discover that extensive exposure to a limited set of training data in terms of class diversity \emph{early} in the training can lead to an irreversible reduction of the capability of a network to learn from more diverse training data. This is in close alignment with existing theories in human development. In contrast, training on a small data set covering all classes \emph{early} in the training can lead to better performance.

Connected through hyperedge HE-1 (induced by source paper "survey of personalization techniques for federated learning"):
  with members:
    - "three approaches for personalization with applications to federated learning"
    - "personalized federated learning for intelligent iot applications a cloud edge based framework"
    - "data selection for federated learning with relevant and irrelevant data at clients"
    - "federated learning of a mixture of global and local models"
    - "salvaging federated learning by local adaptation"
    - "personalized federated learning a meta learning approach"
    - "federated learning with personalization layers"
    - "adaptive gradient based meta learning methods"

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)