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: "distributed multi task learning with shared representation"
  Abstract: We study the problem of distributed multi-task learning with shared representation, where each machine aims to learn a separate, but related, task in an unknown shared low-dimensional subspaces, i.e. when the predictor matrix has low rank. We consider a setting where each task is handled by a different machine, with samples for the task available locally on the machine, and study communication-efficient methods for exploiting the shared structure.

Connected through hyperedge HE-1 (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"
    - "asynchronous multi task learning"
    - "learning laplacian matrix in smooth graph signal representations"

Connected through hyperedge HE-2 (induced by source paper "decentralized multi task learning based on extreme learning machines"):
  with members:
    - "on better exploring and exploiting task relationships in multitask learning joint model and feature learning"
    - "federated multi task learning"
    - "distributed multi task relationship learning"
    - "proximal multitask learning over networks with sparsity inducing coregularization"
    - "a convex formulation for learning task relationships in multi task learning"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-3 (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"
    - "asynchronous multi task learning"
    - "tensor switching networks"
    - "convexified convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "distributed multi task relationship learning"):
  with members:
    - "asynchronous multi task learning"
    - "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)