human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "new inference strategies for solving markov decision processes using reversible jump mcmc"
  Abstract: In this paper we build on previous work which uses inferences techniques, in particular Markov Chain Monte Carlo (MCMC) methods, to solve parameterized control problems. We propose a number of modifications in order to make this approach more practical in general, higher-dimensional spaces. We first introduce a new target distribution which is able to incorporate more reward information from sampled trajectories. We also show how to break strong correlations between the policy parameters and sampled trajectories in order to sample more freely. Finally, we show how to incorporate these techniques in a principled manner to obtain estimates of the optimal policy.

Connected through hyperedge HE-1 (induced by source paper "optimal control of complex systems through variational inference with a discrete event decision process"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "continuous control with deep reinforcement learning"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "optimal vaccine allocation to control epidemic outbreaks in arbitrary networks"
    - "belief propagation for structured decision making"
    - "efficient inference in markov control problems"

Connected through hyperedge HE-2 (induced by source paper "anytime planning for decentralized pomdps using expectation maximization"):
  with members:
    - "the complexity of decentralized control of markov decision processes"
    - "heuristic search value iteration for pomdps"
    - "hierarchical pomdp controller optimization by likelihood maximization"
    - "policy iteration for decentralized control of markov decision processes"
    - "optimal and approximate q value functions for decentralized pomdps"

Connected through hyperedge HE-3 (induced by source paper "portfolio allocation for bayesian optimization"):
  with members:
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "a parameter free hedging algorithm"

Question: Please tell me which class the center node 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)