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: "model predictive policy learning with uncertainty regularization for driving in dense traffic"
  Abstract: Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training. We propose to train a policy by unrolling a learned model of the environment dynamics over multiple time steps while explicitly penalizing two costs: the original cost the policy seeks to optimize, and an uncertainty cost which represents its divergence from the states it is trained on. We measure this second cost by using the uncertainty of the dynamics model about its own predictions, using recent ideas from uncertainty estimation for deep networks. We evaluate our approach using a large-scale observational dataset of driving behavior recorded from traffic cameras, and show that we are able to learn effective driving policies from purely observational data, with no environment interaction.

Connected through hyperedge HE-1 (induced by source paper "behavior planning for connected autonomous vehicles using feedback deep reinforcement learning"):
  with members:
    - "multi agent reinforcement learning a selective overview of theories and algorithms"
    - "control of connected and automated vehicles state of the art and future challenges"
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "virtual to real reinforcement learning for autonomous driving"
    - "end to end driving via conditional imitation learning"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "dream to control learning behaviors by latent imagination"):
  with members:
    - "imagined value gradients model based policy optimization with transferable latent dynamics models"
    - "exploring model based planning with policy networks"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "on variational bounds of mutual information"

Connected through hyperedge HE-3 (induced by source paper "dynamics aware embeddings"):
  with members:
    - "model based reinforcement learning for atari"
    - "learning action representations for reinforcement learning"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "the natural language of actions"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-4 (induced by source paper "multi turn beam search for neural dialogue modeling"):
  with members:
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "pyro deep universal probabilistic programming"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "world models"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "importance of a search strategy in neural dialogue modelling"

Connected through hyperedge HE-5 (induced by source paper "explicit explore exploit algorithms in continuous state spaces"):
  with members:
    - "provably efficient rl with rich observations via latent state decoding"
    - "self supervised exploration via disagreement"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "model based active exploration"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "practical contextual bandits with regression oracles"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-6 (induced by source paper "evaluating task agnostic exploration for fixed batch learning of arbitrary future tasks"):
  with members:
    - "exploration by random network distillation"
    - "model ensemble trust region policy optimization"
    - "off policy deep reinforcement learning without exploration"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "large scale study of curiosity driven learning"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "self supervised exploration via disagreement"):
  with members:
    - "large scale study of curiosity driven learning"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "unity a general platform for intelligent agents"
    - "diversity is all you need learning skills without a reward function"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "parameter space noise for exploration"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "intrinsic motivation and automatic curricula via asymmetric self play"

Connected through hyperedge HE-8 (induced by source paper "estimating risk and uncertainty in deep reinforcement learning"):
  with members:
    - "exploration by random network distillation"
    - "measuring and characterizing generalization in deep reinforcement learning"
    - "information directed exploration for deep reinforcement learning"
    - "mitigating planner overfitting in model based reinforcement learning"
    - "safe reinforcement learning with model uncertainty estimates"
    - "exploration by distributional reinforcement learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics 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)