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: "predictive state representations a new theory for modeling dynamical systems"
  Abstract: Modeling dynamical systems, both for control purposes and to make predictions about their behavior, is ubiquitous in science and engineering. Predictive state representations (PSRs) are a recently introduced class of models for discrete-time dynamical systems. The key idea behind PSRs and the closely related OOMs (Jaeger's observable operator models) is to represent the state of the system as a set of predictions of observable outcomes of experiments one can do in the system. This makes PSRs rather different from history-based models such as nth-order Markov models and hidden-state-based models such as HMMs and POMDPs. We introduce an interesting construct, the systemdynamics matrix, and show how PSRs can be derived simply from it. We also use this construct to show formally that PSRs are more general than both nth-order Markov models and HMMs/POMDPs. Finally, we discuss the main difference between PSRs and OOMs and conclude with directions for future work.

Connected through hyperedge HE-1 (induced by source paper "discovery of useful questions as auxiliary tasks"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "general non linear bellman equations"
    - "on inductive biases in deep reinforcement learning"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "unsupervised meta learning for reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "meta gradient reinforcement learning"
    - "unicorn continual learning with a universal off policy agent"

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 "learning causal state representations of partially observable environments"):
  with members:
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "deep variational reinforcement learning for pomdps"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "extracting automata from recurrent neural networks using queries and counterexamples"
    - "rule extraction algorithm for deep neural networks a review"
    - "deep recurrent q learning for partially observable mdps"

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"
    - "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-5 (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"
    - "efficient learning and planning with compressed predictive states"
    - "heuristic search value iteration for pomdps"
    - "online planning algorithms for pomdps"
    - "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"
    - "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-7 (induced by source paper "expressiveness and learning of hidden quantum markov models"):
  with members:
    - "learning quantum graphical models using constrained gradient descent on the stiefel manifold"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

Connected through hyperedge HE-8 (induced by source paper "decoupling feature extraction from policy learning assessing benefits of state representation learning in goal based robotics"):
  with members:
    - "s rl toolbox environments datasets and evaluation metrics for state representation learning"
    - "large scale study of curiosity driven learning"
    - "state representation learning for control an overview"
    - "deep reinforcement learning that matters"
    - "hindsight experience replay"
    - "pves position velocity encoders for unsupervised learning of structured state representations"
    - "curiosity driven exploration by self supervised prediction"
    - "unsupervised state representation learning with robotic priors a robustness benchmark"

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)