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: "closing the learning planning loop with predictive state representations"
  Abstract: A central problem in artificial intelligence is that of planning to maximize future reward under uncertainty in a partially observable environment. In this paper we propose and demonstrate a novel algorithm which accurately learns a model of such an environment directly from sequences of action-observation pairs. We then close the loop from observations to actions by planning in the learned model and recovering a policy which is near-optimal in the original environment. Specifically, we present an efficient and statistically consistent spectral algorithm for learning the parameters of a Predictive State Representation (PSR). We demonstrate the algorithm by learning a model of a simulated high-dimensional, vision-based mobile robot planning task, and then perform approximate point-based planning in the learned PSR. Analysis of our results shows that the algorithm learns a state space which efficiently captures the essential features of the environment. This representation allows accurate prediction with a small number of parameters, and enables successful and efficient planning.

Connected through hyperedge HE-1 (induced by source paper "unsupervised visuomotor control through distributional planning networks"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "soft actor critic algorithms and applications"
    - "few shot goal inference for visuomotor learning and planning"
    - "unsupervised control through non parametric discriminative rewards"

Connected through hyperedge HE-2 (induced by source paper "harnessing structures for value based planning and reinforcement learning"):
  with members:
    - "me net towards effective adversarial robustness with matrix estimation"
    - "deepmind control suite"
    - "model agnostic time series analysis via matrix estimation"
    - "implicit quantile networks for distributional reinforcement learning"
    - "count based exploration with neural density models"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "high dimensional stochastic optimal control using continuous tensor decompositions"
    - "deep exploration via bootstrapped dqn"

Connected through hyperedge HE-3 (induced by source paper "intelligent autonomous things on the battlefield"):
  with members:
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "robust adversarial reinforcement learning"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "the internet of battle things"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "how do you command an army of intelligent things"
    - "se3 nets learning rigid body motion using deep neural networks"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-4 (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"
    - "efficient learning and planning with compressed predictive states"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "singular value automata and approximate minimization"):
  with members:
    - "a canonical form for weighted automata and applications to approximate minimization"
    - "stability and complexity of minimising probabilistic automata"
    - "reduced rank hidden markov models"
    - "a spectral algorithm for learning hidden markov models"

Connected through hyperedge HE-6 (induced by source paper "efficient planning under partial observability with unnormalized q functions and spectral learning"):
  with members:
    - "efficient learning and planning with compressed predictive states"
    - "predictive state representations a new theory for modeling dynamical systems"
    - "feature hashing for large scale multitask learning"
    - "a monte carlo aixi approximation"

Connected through hyperedge HE-7 (induced by source paper "particle filter networks with application to visual localization"):
  with members:
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "filtering variational objectives"
    - "value prediction network"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-8 (induced by source paper "neural predictive belief representations"):
  with members:
    - "integrating algorithmic planning and deep learning for partially observable navigation"
    - "unsupervised predictive memory in a goal directed agent"
    - "learning awareness models"
    - "representation learning with contrastive predictive coding"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning to act by predicting the future"
    - "recurrent reinforcement learning a hybrid approach"
    - "deep residual learning for image recognition"

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)