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 model based deep reinforcement learning with variational state tabulation"
  Abstract: Modern reinforcement learning algorithms reach super-human performance in many board and video games, but they are sample inefficient, i.e. they typically require significantly more playing experience than humans to reach an equal performance level. To improve sample efficiency, an agent may build a model of the environment and use planning methods to update its policy. In this article we introduce VaST (Variational State Tabulation), which maps an environment with a high-dimensional state space (e.g. the space of visual inputs) to an abstract tabular environment. Prioritized sweeping with small backups, a highly efficient planning method, can then be used to update state-action values. We show how VaST can rapidly learn to maximize reward in tasks like 3D navigation and efficiently adapt to sudden changes in rewards or transition probabilities.

Connected through hyperedge HE-1 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning robotic manipulation through visual planning and acting"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "unsupervised state representation learning in atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "composable planning with attributes"
    - "combined reinforcement learning via abstract representations"

Connected through hyperedge HE-2 (induced by source paper "frequency based search control in dyna"):
  with members:
    - "hill climbing on value estimates for search control in dyna"
    - "a new perspective on machine learning how to do perfect supervised learning"
    - "organizing experience a deeper look at replay mechanisms for sample based planning in continuous state domains"
    - "recurrent world models facilitate policy evolution"
    - "value prediction network"
    - "the predictron end to end learning and planning"
    - "continuous deep q learning with model based acceleration"
    - "self correcting models for model based reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "skill transfer in deep reinforcement learning under morphological heterogeneity"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "synthesized policies for transfer and adaptation across tasks and environments"
    - "learning discrete and continuous factors of data via alternating disentanglement"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "policy transfer with strategy optimization"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "visual reinforcement learning with imagined goals"
    - "bayesian model agnostic meta learning"

Connected through hyperedge HE-4 (induced by source paper "learning robotic manipulation through visual planning and acting"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning robotic assembly from cad"
    - "model ensemble trust region policy optimization"
    - "visual reinforcement learning with imagined goals"
    - "learning plannable representations with causal infogan"
    - "spectral normalization for generative adversarial networks"
    - "bias and generalization in deep generative models an empirical study"
    - "visual robot task planning"

Connected through hyperedge HE-5 (induced by source paper "regression planning networks"):
  with members:
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "zero shot visual imitation"
    - "hierarchical reinforcement learning for zero shot generalization with subtask dependencies"
    - "deep object centric policies for autonomous driving"
    - "making neural programming architectures generalize via recursion"
    - "scene graph generation by iterative message passing"

Connected through hyperedge HE-6 (induced by source paper "tutorial and survey on probabilistic graphical model and variational inference in deep reinforcement learning"):
  with members:
    - "maximum entropy regularized multi goal reinforcement learning"
    - "variational resampling based assessment of deep neural networks under distribution shift"
    - "resampling based assessment of robustness to distribution shift for deep neural networks"
    - "high dimensional restrictive federated model selection with multi objective bayesian optimization over shifted distributions"
    - "reinbo machine learning pipeline search and configuration with bayesian optimization embedded reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"

Connected through hyperedge HE-7 (induced by source paper "learning causal state representations of partially observable environments"):
  with members:
    - "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"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-8 (induced by source paper "hill climbing on value estimates for search control in dyna"):
  with members:
    - "organizing experience a deeper look at replay mechanisms for sample based planning in continuous state domains"
    - "the effect of planning shape on dyna style planning in high dimensional state spaces"
    - "prioritized experience replay"
    - "dyna style planning with linear function approximation and prioritized sweeping"

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)