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: "asynchronous multi task learning"
  Abstract: Many real-world machine learning applications involve several learning tasks which are inter-related. For example, in healthcare domain, we need to learn a predictive model of a certain disease for many hospitals. The models for each hospital may be different because of the inherent differences in the distributions of the patient populations. However, the models are also closely related because of the nature of the learning tasks modeling the same disease. By simultaneously learning all the tasks, multi-task learning (MTL) paradigm performs inductive knowledge transfer among tasks to improve the generalization performance. When datasets for the learning tasks are stored at different locations, it may not always be feasible to transfer the data to provide a data-centralized computing environment due to various practical issues such as high data volume and privacy. In this paper, we propose a principled MTL framework for distributed and asynchronous optimization to address the aforementioned challenges. In our framework, gradient update does not wait for collecting the gradient information from all the tasks. Therefore, the proposed method is very efficient when the communication delay is too high for some task nodes. We show that many regularized MTL formulations can benefit from this framework, including the low-rank MTL for shared subspace learning. Empirical studies on both synthetic and real-world datasets demonstrate the efficiency and effectiveness of the proposed framework.

Connected through hyperedge HE-1 (induced by source paper "safa a semi asynchronous protocol for fast federated learning with low overhead"):
  with members:
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "towards federated learning at scale system design"
    - "distributed deep learning model for intelligent video surveillance systems with edge computing"
    - "a bi layered parallel training architecture for large scale convolutional neural networks"
    - "asynchronous federated optimization"
    - "edge intelligence the confluence of edge computing and artificial intelligence"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "parallel protein community detection in large scale ppi networks based on multi source learning"

Connected through hyperedge HE-2 (induced by source paper "asynchronous online federated learning for edge devices"):
  with members:
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated learning strategies for improving communication efficiency"
    - "leaf a benchmark for federated settings"
    - "adaptive federated learning in resource constrained edge computing systems"
    - "federated learning for mobile keyboard prediction"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "federated learning with non iid data"
    - "attention is all you need"

Connected through hyperedge HE-3 (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"
    - "decentralized collaborative learning of personalized models over networks"
    - "communication efficient learning of deep networks from decentralized data"
    - "distributed multi task learning with shared representation"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-4 (induced by source paper "federated multi task learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "distributed multi task relationship learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "distributed multi task learning with shared representation"
    - "tensor switching networks"
    - "convexified convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "multi agent distributed lifelong learning for collective knowledge acquisition"):
  with members:
    - "distributed multi task relationship learning"
    - "multitask diffusion adaptation over networks"
    - "sparse coding for multitask and transfer learning"
    - "learning task grouping and overlap in multi task learning"
    - "distributed autonomous online learning regrets and intrinsic privacy preserving properties"

Connected through hyperedge HE-6 (induced by source paper "distributed multi task relationship learning"):
  with members:
    - "distributed multi task learning with shared representation"
    - "adding vs averaging in distributed primal dual optimization"
    - "mllib machine learning in apache spark"
    - "communication efficient distributed optimization using an approximate newton type method"
    - "a convex formulation for learning task relationships in multi task learning"
    - "distributed learning communication complexity and privacy"
    - "client server multitask learning from distributed datasets"
    - "clustered multi task learning a convex formulation"

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)