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 "multi agent distributed lifelong learning for collective knowledge acquisition"):
  Abstract: Lifelong machine learning methods acquire knowledge over a series of consecutive tasks, continually building upon their experience. Current lifelong learning algorithms rely upon a single learning agent that has centralized access to all data. In this paper, we extend the idea of lifelong learning from a single agent to a network of multiple agents that collectively learn a series of tasks. Each agent faces some (potentially unique) set of tasks; the key idea is that knowledge learned from these tasks may benefit other agents trying to learn different (but related) tasks. Our Collective Lifelong Learning Algorithm (CoLLA) provides an efficient way for a network of agents to share their learned knowledge in a distributed and decentralized manner, while preserving the privacy of the locally observed data. We provide theoretical guarantees for robust performance of the algorithm and empirically demonstrate that CoLLA outperforms existing approaches for distributed multi-task learning on a variety of data sets.

Includes members:
  Member "distributed multi task relationship learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "branched multi task networks deciding what layers to share")
  Member "asynchronous multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safa a semi asynchronous protocol for fast federated learning with low overhead")
  Member "multitask diffusion adaptation over networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adaptation and learning over networks under subspace constraints part i stability analysis")
  Member "sparse coding for multitask and transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to learn stochastic gradient descent with biased regularization")
  Member "learning task grouping and overlap in multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "federated continual learning with adaptive parameter communication")
  Member "distributed autonomous online learning regrets and intrinsic privacy preserving properties":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "theoretical analysis of divide and conquer erm beyond square loss and rkhs")

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)