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: "reinforcement learning of pomdp s 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 w.r.t. the optimal memoryless policy and efficient scaling with respect to the dimensionality of observation and action spaces.

Connected through hyperedge HE-1 (induced by source paper "regret bound of adaptive control in linear quadratic gaussian lqg systems"):
  with members:
    - "regret minimization in partially observable linear quadratic control"
    - "improper learning for non stochastic control"
    - "learning linear dynamical systems with semi parametric least squares"
    - "robust guarantees for learning an autoregressive filter"
    - "finite sample analysis of stochastic system identification"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"

Connected through hyperedge HE-2 (induced by source paper "regime switching bandits"):
  with members:
    - "a survey on practical applications of multi armed and contextual bandits"
    - "restless hidden markov bandits with linear rewards"
    - "introduction to multi armed bandits"
    - "learning to optimize under non stationarity"
    - "multi armed bandits for correlated markovian environments with smoothed reward feedback"
    - "exploration bonus for regret minimization in undiscounted discrete and continuous markov decision processes"
    - "reinforcement learning of pomdps using spectral methods"
    - "bounded regret for finite armed structured bandits"

Connected through hyperedge HE-3 (induced by source paper "regret minimization in partially observable linear quadratic control"):
  with members:
    - "finite sample analysis of stochastic system identification"
    - "finite time system identification for partially observed lti systems of unknown order"
    - "learning linear quadratic regulators efficiently with only t regret"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "spectral filtering for general linear dynamical systems"
    - "model free linear quadratic control via reduction to expert prediction"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"
    - "learning without mixing towards a sharp analysis of linear system identification"

Connected through hyperedge HE-4 (induced by source paper "can agents learn by analogy an inferable model for pac reinforcement learning"):
  with members:
    - "provably efficient reinforcement learning with linear function approximation"
    - "sample optimal parametric q learning using linearly additive features"
    - "reinforcement leaning in feature space matrix bandit kernels and regret bound"
    - "sample optimal parametric q learning with linear transition models"
    - "policy certificates towards accountable reinforcement learning"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "reinforcement learning in rich observation mdps using spectral methods"
    - "reinforcement learning of pomdps using spectral methods"

Connected through hyperedge HE-5 (induced by source paper "regularized learning for domain adaptation under label shifts"):
  with members:
    - "detecting and correcting for label shift with black box predictors"
    - "active learning for cost sensitive classification"
    - "reinforcement learning of pomdps using spectral methods"
    - "mixture proportion estimation via kernel embedding of distributions"
    - "deep residual learning for image recognition"
    - "statistical linear estimation with penalized estimators an application to reinforcement learning"
    - "a method of moments for mixture models and hidden markov models"
    - "on causal and anticausal learning"

Connected through hyperedge HE-6 (induced by source paper "when does non orthogonal tensor decomposition have no spurious local minima"):
  with members:
    - "on the optimization landscape of tensor decompositions"
    - "learning mixtures of gaussians in high dimensions"
    - "guaranteed non orthogonal tensor decomposition via alternating rank 1 updates"
    - "a tensor approach to learning mixed membership community models"
    - "tensor decompositions for learning latent variable models"
    - "most tensor problems are np hard"

Connected through hyperedge HE-7 (induced by source paper "trust region policy optimization of pomdps"):
  with members:
    - "signsgd compressed optimisation for non convex problems"
    - "experimental results reinforcement learning of pomdps using spectral methods"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "proximal policy optimization algorithms"
    - "reinforcement learning of pomdps using spectral methods"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "infinite horizon policy gradient estimation"

Connected through hyperedge HE-8 (induced by source paper "experimental results reinforcement learning of pomdps using spectral methods"):
  with members:
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "open problem approximate planning of pomdps in the class of memoryless policies"
    - "efficient learning and planning with compressed predictive states"
    - "nonparametric estimation of multi view latent variable models"
    - "playing atari with deep reinforcement learning"
    - "a method of moments for mixture models and hidden markov models"
    - "closing the learning planning loop with predictive state representations"

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.AI(Artificial Intelligence)