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: "efficient learning and planning with compressed predictive states"
  Abstract: Predictive state representations (PSRs) offer an expressive framework for modelling partially observable systems. By compactly representing systems as functions of observable quantities, the PSR learning approach avoids using local-minima prone expectation-maximization and instead employs a globally optimal moment-based algorithm. Moreover, since PSRs do not require a predetermined latent state structure as an input, they offer an attractive framework for model-based reinforcement learning when agents must plan without a priori access to a system model. Unfortunately, the expressiveness of PSRs comes with significant computational cost, and this cost is a major factor inhibiting the use of PSRs in applications. In order to alleviate this shortcoming, we introduce the notion of compressed PSRs (CPSRs). The CPSR learning approach combines recent advancements in dimensionality reduction, incremental matrix decomposition, and compressed sensing. We show how this approach provides a principled avenue for learning accurate approximations of PSRs, drastically reducing the computational costs associated with learning while also providing effective regularization. Going further, we propose a planning framework which exploits these learned models. And we show that this approach facilitates model-learning and planning in large complex partially observable domains, a task that is infeasible without the principled use of compression.

Connected through hyperedge HE-1 (induced by source paper "combining offline models and online monte carlo tree search for planning from scratch"):
  with members:
    - "bayesian reinforcement learning in factored pomdps"
    - "learning in pomdps with monte carlo tree search"
    - "despot online pomdp planning with regularization"
    - "bayesian reinforcement learning a survey"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "heuristic search value iteration for pomdps"
    - "learning to make predictions in partially observable environments without a generative model"
    - "closing the learning planning loop with predictive state representations"

Connected through hyperedge HE-2 (induced by source paper "online learning and planning in partially observable domains without prior knowledge"):
  with members:
    - "combining offline models and online monte carlo tree search for planning from scratch"
    - "bayesian reinforcement learning in factored pomdps"
    - "learning in pomdps with monte carlo tree search"
    - "bayesian reinforcement learning a survey"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "heuristic search value iteration for pomdps"
    - "online planning algorithms for pomdps"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-3 (induced by source paper "efficient planning under partial observability with unnormalized q functions and spectral learning"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "feature hashing for large scale multitask learning"
    - "closing the learning planning loop with predictive state representations"
    - "a monte carlo aixi approximation"

Connected through hyperedge HE-4 (induced by source paper "a consistent method for learning ooms from asymptotically stationary time series data containing missing values"):
  with members:
    - "spectral learning of dynamic systems from nonequilibrium data"
    - "reinforcement learning of pomdps using spectral methods"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "tensor decompositions for learning latent variable models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "a spectral algorithm for learning hidden markov models"
    - "the ergodic decomposition of asymptotically mean stationary random sources"

Connected through hyperedge HE-5 (induced by source paper "organizing experience a deeper look at replay mechanisms for sample based planning in continuous state domains"):
  with members:
    - "modelling transition dynamics in mdps with rkhs embeddings"
    - "dyna style planning with linear function approximation and prioritized sweeping"

Connected through hyperedge HE-6 (induced by source paper "experimental results reinforcement learning of pomdps using spectral methods"):
  with members:
    - "reinforcement learning of pomdp s using spectral methods"
    - "contextual mdps for pac reinforcement learning with rich observations"
    - "open problem approximate planning of pomdps in the class of memoryless policies"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning of pomdps using spectral methods"):
  with members:
    - "a pac rl algorithm for episodic pomdps"
    - "online learning and optimization of markov jump affine models"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "selecting bases in spectral learning of predictive state representations via model entropy"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "learning to make predictions in partially observable environments without a generative model"
    - "closing the learning planning loop with predictive state representations"
    - "a spectral algorithm for learning 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.LG(Machine Learning)