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: "online learning and optimization of markov jump affine models"
  Abstract: The problem of online learning and optimization of unknown Markov jump affine models is considered. An online learning policy, referred to as Markovian simultaneous perturbations stochastic approximation (MSPSA), is proposed for two different optimization objectives: (i) the quadratic cost minimization of the regulation problem and (ii) the revenue (profit) maximization problem. It is shown that the regret of MSPSA grows at the order of the square root of the learning horizon. Furthermore, by the use of van Trees inequality, it is shown that the regret of any policy grows no slower than that of MSPSA, making MSPSA an order optimal learning policy. In addition, it is also shown that the MSPSA policy converges to the optimal control input almost surely as well as in the mean square sense. Simulation results are presented to illustrate the regret growth rate of MSPSA and to show that MSPSA can offer significant gain over the greedy certainty equivalent approach.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning of pomdps using spectral methods"):
  with members:
    - "a pac rl algorithm for episodic pomdps"
    - "selecting near optimal approximate state representations in reinforcement learning"
    - "efficient learning and planning with compressed predictive states"
    - "nonparametric estimation of multi view latent variable models"
    - "on learning parametric output hmms"
    - "pegasus a policy search method for large mdps and pomdps"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning of pomdp s using spectral methods"):
  with members:
    - "a pac rl algorithm for episodic pomdps"
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "selecting near optimal approximate state representations in reinforcement learning"
    - "nonparametric estimation of multi view latent variable models"
    - "on learning parametric output hmms"
    - "pegasus a policy search method for large mdps and pomdps"
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"

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.IT(Information Theory)