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 "contextual mdps for pac reinforcement learning with rich observations"):
  Abstract: We propose and study a new tractable model for reinforcement learning with rich observations called Contextual-MDPs, generalizing contextual bandits to sequential decision making. These models require an agent to take actions based on observations (features) with the goal of achieving long-term performance competitive with a large set of policies. To avoid barriers to sample-efficient learning associated with large observation spaces and general POMDPs, Contextual-MDPs can be summarized by a small number of hidden states and long-term rewards are predictable by a reactive function class. In this setting, we design a new reinforcement learning algorithm that engages in global exploration and analyze its sample complexity. We prove that the algorithm learns near optimal behavior after a number of episodes that is polynomial in all relevant parameters, logarithmic in the number of policies, and independent of the size of the observation space. This represents an exponential improvement over all existing alternative approaches and provides theoretical justification for reinforcement learning with function approximation.

Includes members:
  Member "reinforcement learning of pomdp s using spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "online learning in mdps with side information":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sample complexity of reinforcement learning using linearly combined model ensembles")
  Member "learning finite state controllers for partially observable environments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "counterexample guided strategy improvement for pomdps using recurrent neural networks")
  Member "predictive state representations a new theory for modeling dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discovery of useful questions as auxiliary tasks")
  Member "contextual bandit learning with predictable rewards":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")

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)