human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "propagation networks for model based control under partial observation"):
  Abstract: There has been an increasing interest in learning dynamics simulators for model-based control. Compared with off-the-shelf physics engines, a learnable simulator can quickly adapt to unseen objects, scenes, and tasks. However, existing models like interaction networks only work for fully observable systems; they also only consider pairwise interactions within a single time step, both restricting their use in practical systems. We introduce Propagation Networks (PropNet), a differentiable, learnable dynamics model that handles partially observable scenarios and enables instantaneous propagation of signals beyond pairwise interactions. Experiments show that our propagation networks not only outperform current learnable physics engines in forward simulation, but also achieve superior performance on various control tasks. Compared with existing model-free deep reinforcement learning algorithms, model-based control with propagation networks is more accurate, efficient, and generalizable to new, partially observable scenes and tasks.

Includes members:
  Member "fundamental limitations in performance and interpretability of common planar rigid body contact models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks")
  Member "universal planning networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning functionally decomposed hierarchies for continuous control tasks")
  Member "graph networks as learnable physics engines for inference and control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmarking graph neural networks")
  Member "simple recurrent units for highly parallelizable recurrence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "value prediction network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "learning model based planning from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for cyber security")
  Member "neural network dynamics for model based deep reinforcement learning with model free fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "neural message passing for quantum chemistry":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")

Question: Please tell me which class the source paper 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)