human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "distributed multi task relationship learning"):
  Abstract: In this paper, we propose a distributed multi-task learning framework that simultaneously learns predictive models for each task as well as task relationships between tasks alternatingly in the parameter server paradigm. In our framework, we first offer a general dual form for a family of regularized multi-task relationship learning methods. Subsequently, we propose a communication-efficient primal-dual distributed optimization algorithm to solve the dual problem by carefully designing local subproblems to make the dual problem decomposable. Moreover, we provide a theoretical convergence analysis for the proposed algorithm, which is specific for distributed multi-task relationship learning. We conduct extensive experiments on both synthetic and real-world datasets to evaluate our proposed framework in terms of scalability, effectiveness, and convergence.

Includes members:
  Member "asynchronous multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "safa a semi asynchronous protocol for fast federated learning with low overhead")
  Member "distributed multi task learning with shared representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fully decentralized joint learning of personalized models and collaboration graphs")
  Member "adding vs averaging in distributed primal dual optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "mllib machine learning in apache spark":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi layer optimizations for end to end data analytics")
  Member "communication efficient distributed optimization using an approximate newton type method":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "a convex formulation for learning task relationships in multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a simple general approach to balance task difficulty in multi task learning")
  Member "distributed learning communication complexity and privacy":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on distributed machine learning")
  Member "client server multitask learning from distributed datasets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "asynchronous multi task learning")

Question: Please tell me which class the source paper 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)