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 "rapidly personalizing mobile health treatment policies with limited data"):
  Abstract: In mobile health (mHealth), reinforcement learning algorithms that adapt to one's context without learning personalized policies might fail to distinguish between the needs of individuals. Yet the high amount of noise due to the in situ delivery of mHealth interventions can cripple the ability of an algorithm to learn when given access to only a single user's data, making personalization challenging. We present IntelligentPooling, which learns personalized policies via an adaptive, principled use of other users' data. We show that IntelligentPooling achieves an average of 26% lower regret than state-of-the-art across all generative models. Additionally, we inspect the behavior of this approach in a live clinical trial, demonstrating its ability to learn from even a small group of users.

Includes members:
  Member "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization")
  Member "encouraging physical activity in patients with diabetes intervention using a reinforcement learning system":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning in healthcare a survey")
  Member "on kernelized multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixed strategies for robust optimization of unknown objectives")
  Member "context aware bandits":
    also connected through other hyperedges:
      (none)
  Member "a gang of bandits":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "contextual bandit based personalized recommendation with time varying user interests")
  Member "sparse gaussian processes for multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalized spectral mixture kernels for multi task gaussian processes")
  Member "information theoretic regret bounds for gaussian process optimization in the bandit setting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient and robust algorithms for adversarial linear contextual bandits")
  Member "analysis of thompson sampling for the multi armed bandit problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "effective diversity in population based reinforcement 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)