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 "on oracle efficient pac rl with rich observations"):
  Abstract: We study the computational tractability of PAC reinforcement learning with rich observations. We present new provably sample-efficient algorithms for environments with deterministic hidden state dynamics and stochastic rich observations. These methods operate in an oracle model of computation -- accessing policy and value function classes exclusively through standard optimization primitives -- and therefore represent computationally efficient alternatives to prior algorithms that require enumeration. With stochastic hidden state dynamics, we prove that the only known sample-efficient algorithm, OLIVE, cannot be implemented in the oracle model. We also present several examples that illustrate fundamental challenges of tractable PAC reinforcement learning in such general settings.

Includes members:
  Member "sbeed convergent reinforcement learning with nonlinear function approximation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "provably convergent two timescale off policy actor critic with function approximation")
  Member "unifying pac and regret uniform pac bounds for episodic reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "reinforcement learning in rich observation mdps using spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "can agents learn by analogy an inferable model for pac reinforcement learning")
  Member "a pac rl algorithm for episodic pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on polynomial time pac reinforcement learning with rich observations")
  Member "reinforcement learning of pomdps using spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "contextual decision processes with low bellman rank are pac learnable":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning zero sum simultaneous move markov games using function approximation and correlated equilibrium")
  Member "pac reinforcement learning with rich observations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "learning to search better than your teacher":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")

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)