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 "monte carlo bayesian reinforcement learning"):
  Abstract: Bayesian reinforcement learning (BRL) encodes prior knowledge of the world in a model and represents uncertainty in model parameters by maintaining a probability distribution over them. This paper presents Monte Carlo BRL (MC-BRL), a simple and general approach to BRL. MC-BRL samples a priori a finite set of hypotheses for the model parameter values and forms a discrete partially observable Markov decision process (POMDP) whose state space is a cross product of the state space for the reinforcement learning task and the sampled model parameter space. The POMDP does not require conjugate distributions for belief representation, as earlier works do, and can be solved relatively easily with point-based approximation algorithms. MC-BRL naturally handles both fully and partially observable worlds. Theoretical and experimental results show that the discrete POMDP approximates the underlying BRL task well with guaranteed performance.

Includes members:
  Member "model based bayesian exploration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "benchmarking model based reinforcement learning")
  Member "pegasus a policy search method for large mdps and pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fidi rl incorporating deep reinforcement learning with finite difference policy search for efficient learning of continuous control")
  Member "point based pomdp algorithms improved analysis and implementation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing sequential medical treatments with auto encoding heuristic search in pomdps")
  Member "model based bayesian reinforcement learning in large structured domains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "expanding the active inference landscape more intrinsic motivations in the perception action loop")
  Member "a bayesian sampling approach to exploration in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "worst case regret bounds for exploration via randomized value functions")
  Member "on some winning strategies for the iterated prisoner s dilemma or mr nice guy and the cosa nostra":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recognising and evaluating the effectiveness of extortion in the iterated prisoner s dilemma")

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)