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: "decentralized collaborative learning of personalized models over networks"
  Abstract: We consider a set of learning agents in a collaborative peer-to-peer network, where each agent learns a personalized model according to its own learning objective. The question addressed in this paper is: how can agents improve upon their locally trained model by communicating with other agents that have similar objectives? We introduce and analyze two asynchronous gossip algorithms running in a fully decentralized manner. Our first approach, inspired from label propagation, aims to smooth pre-trained local models over the network while accounting for the confidence that each agent has in its initial model. In our second approach, agents jointly learn and propagate their model by making iterative updates based on both their local dataset and the behavior of their neighbors. To optimize this challenging objective, our decentralized algorithm is based on ADMM.

Connected through hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication"):
  with members:
    - "deepcabac a universal compression algorithm for deep neural networks"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "advances and open problems in federated learning"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "improving federated learning personalization via model agnostic meta learning"
    - "hierarchical federated learning across heterogeneous cellular networks"

Connected through hyperedge HE-2 (induced by source paper "key protected classification for collaborative learning"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "adversarial attacks and defences a survey"
    - "privacy preserving machine learning through data obfuscation"
    - "differentially private generative adversarial network"
    - "efficient deep learning on multi source private data"
    - "personalized and private peer to peer machine learning"

Connected through hyperedge HE-3 (induced by source paper "central server free federated learning over single sided trust social networks"):
  with members:
    - "decentralized online learning take benefits from others data without sharing your own to track global trend"
    - "introduction to online convex optimization"
    - "federated machine learning concept and applications"
    - "asynchronous subgradient push"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "federated learning strategies for improving communication efficiency"
    - "don t use large mini batches use local sgd"
    - "stochastic gradient push for distributed deep learning"

Connected through hyperedge HE-4 (induced by source paper "who started this rumor quantifying the natural differential privacy guarantees of gossip protocols"):
  with members:
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "stochastic gradient push for distributed deep learning"
    - "distributed differential privacy via shuffling"
    - "personalized and private peer to peer machine learning"
    - "how to discreetly spread a rumor in a crowd"
    - "communication efficient learning of deep networks from decentralized data"
    - "hiding the rumor source"

Connected through hyperedge HE-5 (induced by source paper "fully decentralized joint learning of personalized models and collaboration graphs"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "djam distributed jacobi asynchronous method for learning personal models"
    - "personalized and private peer to peer machine learning"
    - "federated multi task learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "asynchronous multi task learning"
    - "distributed multi task learning with shared representation"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-6 (induced by source paper "djam distributed jacobi asynchronous method for learning personal models"):
  with members:
    - "personalized and private peer to peer machine learning"
    - "network lasso clustering and optimization in large graphs"
    - "linear convergence rate of a class of distributed augmented lagrangian algorithms"
    - "multitask diffusion adaptation over networks"

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)