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 "expectation optimization with probabilistic guarantees in pomdps with discounted sum objectives"):
  Abstract: Partially-observable Markov decision processes (POMDPs) with discounted-sum payoff are a standard framework to model a wide range of problems related to decision making under uncertainty. Traditionally, the goal has been to obtain policies that optimize the expectation of the discounted-sum payoff. A key drawback of the expectation measure is that even low probability events with extreme payoff can significantly affect the expectation, and thus the obtained policies are not necessarily risk-averse. An alternate approach is to optimize the probability that the payoff is above a certain threshold, which allows obtaining risk-averse policies, but ignores optimization of the expectation. We consider the expectation optimization with probabilistic guarantee (EOPG) problem, where the goal is to optimize the expectation ensuring that the payoff is above a given threshold with at least a specified probability. We present several results on the EOPG problem, including the first algorithm to solve it.

Includes members:
  Member "variations on the stochastic shortest path problem":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning of risk constrained policies in markov decision processes")
  Member "optimal cost almost sure reachability in pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "counterexample driven synthesis for probabilistic program sketches")
  Member "meet your expectations with guarantees beyond worst case synthesis in quantitative games":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "games where you can play optimally with finite memory")
  Member "heuristic search value iteration for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "point based methods for model checking in partially observable markov decision processes")
  Member "online planning algorithms for pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning for pomdp partitioned rollout and policy iteration with application to autonomous sequential repair problems")
  Member "reinforcement learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual 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.AI(Artificial Intelligence)