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 planning with discrete and continuous actions"
  Abstract: Action planning using learned and differentiable forward models of the world is a general approach which has a number of desirable properties, including improved sample complexity over model-free RL methods, reuse of learned models across different tasks, and the ability to perform efficient gradient-based optimization in continuous action spaces. However, this approach does not apply straightforwardly when the action space is discrete. In this work, we show that it is in fact possible to effectively perform planning via backprop in discrete action spaces, using a simple paramaterization of the actions vectors on the simplex combined with input noise when training the forward model. Our experiments show that this approach can match or outperform model-free RL and discrete planning methods on gridworld navigation tasks in terms of performance and/or planning time while using limited environment interactions, and can additionally be used to perform model-based control in a challenging new task where the action space combines discrete and continuous actions. We furthermore propose a policy distillation approach which yields a fast policy network which can be used at inference time, removing the need for an iterative planning procedure.

Connected through hyperedge HE-1 (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 predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"

Connected through hyperedge HE-2 (induced by source paper "mapping state space using landmarks for universal goal reaching"):
  with members:
    - "towards learning abstract representations for locomotion planning in high dimensional state spaces"
    - "robot motion planning in learned latent spaces"
    - "hierarchical reinforcement learning with hindsight"
    - "composable planning with attributes"
    - "temporal difference models model free deep rl for model based control"
    - "learning latent dynamics for planning from pixels"
    - "semi parametric topological memory for navigation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"

Connected through hyperedge HE-3 (induced by source paper "continual learning using world models for pseudo rehearsal"):
  with members:
    - "model based reinforcement learning for atari"
    - "complementary learning for overcoming catastrophic forgetting using experience replay"
    - "on first order meta learning algorithms"
    - "learning latent dynamics for planning from pixels"
    - "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"
    - "deep generative dual memory network for continual learning"
    - "recurrent world models facilitate policy evolution"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-4 (induced by source paper "hindsight credit assignment"):
  with members:
    - "credit assignment techniques in stochastic computation graphs"
    - "optimizing agent behavior over long time scales by transporting value"
    - "representation learning with contrastive predictive coding"
    - "hindsight experience replay"
    - "variational intrinsic control"
    - "gradient estimation using stochastic computation graphs"
    - "trust region policy optimization"
    - "learning continuous control policies by stochastic value gradients"

Connected through hyperedge HE-5 (induced by source paper "learning latent dynamics for planning from pixels"):
  with members:
    - "universal planning networks"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "stochastic video generation with a learned prior"
    - "learning and querying fast generative models for reinforcement learning"
    - "unsupervised predictive memory in a goal directed agent"

Connected through hyperedge HE-6 (induced by source paper "universal planning networks"):
  with members:
    - "mpc inspired neural network policies for sequential decision making"
    - "zero shot visual imitation"
    - "hindsight experience replay"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "one shot visual imitation learning via meta learning"
    - "value prediction network"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-7 (induced by source paper "dynamic planning networks"):
  with members:
    - "universal planning networks"
    - "combined reinforcement learning via abstract representations"
    - "learning to search with mctsnets"
    - "world models"
    - "parameter space noise for exploration"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "value prediction network"
    - "learning model based planning from scratch"

Connected through hyperedge HE-8 (induced by source paper "learning and querying fast generative models for reinforcement learning"):
  with members:
    - "rainbow combining improvements in deep reinforcement learning"
    - "stochastic variational video prediction"
    - "value prediction network"
    - "hybrid reward architecture for reinforcement learning"
    - "recurrent environment simulators"
    - "imagination augmented agents for deep reinforcement learning"
    - "deep visual foresight for planning robot motion"
    - "the predictron end to end learning and planning"

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)