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 "context aware hierarchical online learning for performance maximization in mobile crowdsourcing"):
  Abstract: In mobile crowdsourcing (MCS), mobile users accomplish outsourced human intelligence tasks. MCS requires an appropriate task assignment strategy, since different workers may have different performance in terms of acceptance rate and quality. Task assignment is challenging, since a worker’s performance 1) may fluctuate, depending on both the worker’s current personal context and the task context and 2) is not known a priori, but has to be learned over time. Moreover, learning context-specific worker performance requires access to context information, which may not be available at a central entity due to communication overhead or privacy concerns. In addition, evaluating worker performance might require costly quality assessments. In this paper, we propose a context-aware hierarchical online learning algorithm addressing the problem of performance maximization in MCS. In our algorithm, a local controller (LC) in the mobile device of a worker regularly observes the worker’s context, her/his decisions to accept or decline tasks and the quality in completing tasks. Based on these observations, the LC regularly estimates the worker’s context-specific performance. The mobile crowdsourcing platform (MCSP) then selects workers based on performance estimates received from the LCs. This hierarchical approach enables the LCs to learn context-specific worker performance and it enables the MCSP to select suitable workers. In addition, our algorithm preserves worker context locally, and it keeps the number of required quality assessments low. We prove that our algorithm converges to the optimal task assignment strategy. Moreover, the algorithm outperforms simpler task assignment strategies in experiments based on synthetic and real data.

Includes members:
  Member "context aware proactive content caching with service differentiation in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aoi delay tradeoff in mobile edge caching with freshness aware content refreshing")
  Member "dynamic assessments matching and allocation of tasks":
    also connected through other hyperedges:
      (none)
  Member "distributed online learning in social recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "social learning in multi agent multi armed bandits")
  Member "online clustering of bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems")
  Member "resourceful contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "advances in bandits with knapsacks")
  Member "distributed online learning via cooperative contextual bandits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense")
  Member "thompson sampling for contextual bandits with linear payoffs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural contextual bandits with ucb based exploration")
  Member "fast reinforcement learning for energy efficient wireless communication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards tactile internet in beyond 5g era recent advances current issues and future directions")

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)