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: "an investigation of model free planning"
  Abstract: The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity. For an RL agent to address these challenges, it is essential that it can plan effectively. Prior work has typically utilized an explicit model of the environment, combined with a specific planning algorithm (such as tree search). More recently, a new family of methods have been proposed that learn how to plan, by providing the structure for planning via an inductive bias in the function approximator (such as a tree structured neural network), trained end-to-end by a model-free RL algorithm. In this paper, we go even further, and demonstrate empirically that an entirely model-free approach, without special structure beyond standard neural network components such as convolutional networks and LSTMs, can learn to exhibit many of the characteristics typically associated with a model-based planner. We measure our agent's effectiveness at planning in terms of its ability to generalize across a combinatorial and irreversible state space, its data efficiency, and its ability to utilize additional thinking time. We find that our agent has many of the characteristics that one might expect to find in a planning algorithm. Furthermore, it exceeds the state-of-the-art in challenging combinatorial domains such as Sokoban and outperforms other model-free approaches that utilize strong inductive biases toward planning.

Connected through hyperedge HE-1 (induced by source paper "offworld gym open access physical robotics environment for real world reinforcement learning benchmark and research"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "learning latent state representation for speeding up exploration"
    - "go explore a new approach for hard exploration problems"
    - "pyrep bringing v rep to deep robot learning"
    - "self supervised exploration via disagreement"
    - "provably efficient imitation learning from observation alone"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "challenges of real world reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "planning with goal conditioned policies"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "diagnosing and enhancing vae models"
    - "model based reinforcement learning for atari"
    - "temporal difference models model free deep rl for model based control"
    - "learning latent dynamics for planning from pixels"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-3 (induced by source paper "combining q learning and search with amortized value estimates"):
  with members:
    - "action guidance with mcts for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "policy gradient search online planning and expert iteration without search trees"
    - "benchmarking model based reinforcement learning"
    - "unsupervised visuomotor control through distributional planning networks"
    - "structured agents for physical construction"
    - "universal planning networks"
    - "model ensemble trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "causally correct partial models for reinforcement learning"):
  with members:
    - "causal discovery with reinforcement learning"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "combating the compounding error problem with a multi step model"
    - "model based reinforcement learning for atari"
    - "when to trust your model model based policy optimization"
    - "shaping belief states with generative environment models for rl"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-5 (induced by source paper "logic and the 2 simplicial transformer"):
  with members:
    - "neural logic machines"
    - "universal transformers"
    - "generating long sequences with sparse transformers"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "relational recurrent neural networks"
    - "generalisation of structural knowledge in the hippocampal entorhinal system"
    - "deep reinforcement learning that matters"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "the principle of unchanged optimality in reinforcement learning generalization"):
  with members:
    - "on the generalization gap in reparameterizable reinforcement learning"
    - "model based reinforcement learning for atari"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "natural environment benchmarks for reinforcement learning"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "quantifying generalization in reinforcement learning"
    - "unsupervised predictive memory in a goal directed agent"
    - "gotta learn fast a new benchmark for generalization in rl"

Connected through hyperedge HE-7 (induced by source paper "uncertainty sensitive learning and planning with ensembles"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "model based reinforcement learning for atari"
    - "adaptive online planning for continual lifelong learning"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "single agent policy tree search with guarantees"
    - "self imitation learning"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-8 (induced by source paper "value driven hindsight modelling"):
  with members:
    - "mastering atari go chess and shogi by planning with a learned model"
    - "deepmdp learning continuous latent space models for representation learning"
    - "credit assignment techniques in stochastic computation graphs"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "woulda coulda shoulda counterfactually guided policy search"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "value prediction network"
    - "asymmetric actor critic for image based robot learning"

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)