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 based reinforcement learning with parametrized physical models and optimism driven exploration"
  Abstract: In this paper, we present a robotic model-based reinforcement learning method that combines ideas from model identification and model predictive control. We use a feature-based representation of the dynamics that allows the dynamics model to be fitted with a simple least squares procedure, and the features are identified from a high-level specification of the robot's morphology, consisting of the number and connectivity structure of its links. Model predictive control is then used to choose the actions under an optimistic model of the dynamics, which produces an efficient and goal-directed exploration strategy. We present real time experimental results on standard benchmark problems involving the pendulum, cartpole, and double pendulum systems. Experiments indicate that our method is able to learn a range of benchmark tasks substantially faster than the previous best methods. To evaluate our approach on a realistic robotic control task, we also demonstrate real time control of a simulated 7 degree of freedom arm.

Connected through hyperedge HE-1 (induced by source paper "scoring aggregating planning learning task agnostic priors from interactions and sparse rewards for zero shot generalization"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "model based reinforcement learning for atari"
    - "mapping state space using landmarks for universal goal reaching"
    - "object oriented dynamics predictor"
    - "inverse reinforcement learning for video games"
    - "grasp2vec learning object representations from self supervised grasping"
    - "count based exploration with the successor representation"

Connected through hyperedge HE-2 (induced by source paper "shaping belief states with generative environment models for rl"):
  with members:
    - "conditional adversarial generative flow for controllable image synthesis"
    - "preventing posterior collapse with delta vaes"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "learning attractor dynamics for generative memory"
    - "geometric consistency for self supervised end to end visual odometry"
    - "learning and querying fast generative models for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "task agnostic dynamics priors for deep reinforcement learning"):
  with members:
    - "a study on overfitting in deep reinforcement learning"
    - "investigating human priors for playing video games"
    - "flexible neural representation for physics prediction"
    - "decoupling dynamics and reward for transfer learning"
    - "model based value estimation for efficient model free reinforcement learning"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "eigenoption discovery through the deep successor representation"
    - "visual interaction networks"

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"
    - "an investigation of model free planning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "the tools challenge rapid trial and error learning in physical problem solving"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "model based reinforcement learning for atari"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "keyin discovering subgoal structure with keyframe based video prediction"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning latent dynamics for planning from pixels"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "meta reinforcement learning of structured exploration strategies"

Connected through hyperedge HE-6 (induced by source paper "online gaussian process state space models learning and planning for partially observable dynamical systems"):
  with members:
    - "variational gaussian process state space models"

Connected through hyperedge HE-7 (induced by source paper "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"):
  with members:
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "dual policy iteration"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "zero shot visual imitation"
    - "graph networks as learnable physics engines for inference and control"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"

Connected through hyperedge HE-8 (induced by source paper "learning based model predictive control for safe exploration"):
  with members:
    - "linear model predictive safety certification for learning based control"
    - "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"
    - "a provably correct mpc approach to safety control of urban traffic networks"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"

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)