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 "combining offline models and online monte carlo tree search for planning from scratch"):
  Abstract: Planning in stochastic and partially observable environments is a central issue in artificial intelligence. One commonly used technique for solving such a problem is by constructing an accurate model firstly. Although some recent approaches have been proposed for learning optimal behaviour under model uncertainty, prior knowledge about the environment is still needed to guarantee the performance of the proposed algorithms. With the benefits of the Predictive State Representations~(PSRs) approach for state representation and model prediction, in this paper, we introduce an approach for planning from scratch, where an offline PSR model is firstly learned and then combined with online Monte-Carlo tree search for planning with model uncertainty. By comparing with the state-of-the-art approach of planning with model uncertainty, we demonstrated the effectiveness of the proposed approaches along with the proof of their convergence. The effectiveness and scalability of our proposed approach are also tested on the RockSample problem, which are infeasible for the state-of-the-art BA-POMDP based approaches.

Includes members:
  Member "bayesian reinforcement learning in factored pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inducing cooperation via team regret minimization based multi agent deep reinforcement learning")
  Member "learning in pomdps with monte carlo tree search":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "integrating acting planning and learning in hierarchical operational models")
  Member "despot online pomdp planning with regularization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient uncertainty aware decision making for automated driving using guided branching")
  Member "bayesian reinforcement learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning curiosity algorithms")
  Member "efficient learning and planning with compressed predictive states":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online learning and planning in partially observable domains without prior knowledge")
  Member "predictive state representations a new theory for modeling dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "discovery of useful questions as auxiliary tasks")
  Member "heuristic search value iteration for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "point based methods for model checking in partially observable markov decision processes")
  Member "learning to make predictions in partially observable environments without a generative model":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "selecting bases in spectral learning of predictive state representations via model entropy")

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.AI(Artificial Intelligence)