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 "experimental results 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 epochs, in each epoch we employ spectral techniques to learn the POMDP parameters from a trajectory generated by a fixed policy. At the end of the epoch, 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 "reinforcement learning of pomdp s using spectral methods":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems")
  Member "contextual mdps for pac reinforcement learning with rich observations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "autonomous aerial cinematography in unstructured environments with learned artistic decision making")
  Member "open problem approximate planning of pomdps in the class of memoryless policies":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reinforcement learning in rich observation mdps using spectral methods")
  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 "playing atari with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "a method of moments for mixture models and hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provable meta learning of linear representations")
  Member "closing the learning planning loop with predictive state representations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised visuomotor control through distributional planning networks")

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)