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 "reinforcement learning of pomdps using spectral methods"):
  Abstract: We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have been previously employed for consistent learning of (passive) latent variable models such as hidden Markov models, POMDPs are more challenging since the learner interacts with the environment and possibly changes the future observations in the process. We devise a learning algorithm running through episodes, in each episode we employ spectral techniques to learn the POMDP parameters from a trajectory generated by a fixed policy. At the end of the episode, an optimization oracle returns the optimal memoryless planning policy which maximizes the expected reward based on the estimated POMDP model. We prove an order-optimal regret bound with respect to the optimal memoryless policy and efficient scaling with respect to the dimensionality of observation and action spaces.

Includes members:
  Member "a pac rl algorithm for episodic pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on oracle efficient pac rl with rich observations")
  Member "online learning and optimization of markov jump affine models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reinforcement learning of pomdp s using spectral methods")
  Member "selecting near optimal approximate state representations in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provably efficient rl with rich observations via latent state decoding")
  Member "efficient learning and planning with compressed predictive states":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "combining offline models and online monte carlo tree search for planning from scratch")
  Member "nonparametric estimation of multi view latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "spectral learning of binomial hmms for dna methylation data")
  Member "on learning parametric output hmms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "divergence based motivation for online em and combining hidden variable models")
  Member "pegasus a policy search method for large mdps and pomdps":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fidi rl incorporating deep reinforcement learning with finite difference policy search for efficient learning of continuous control")
  Member "tensor decompositions for learning latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "convolutional tensor train lstm for spatio temporal learning")

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.AI(Artificial Intelligence)