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 "value propagation networks"):
  Abstract: We present Value Propagation (VProp), a parameter-efficient differentiable planning module built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments. Furthermore, we show that the module enables learning to plan when the environment also includes stochastic elements, providing a cost-efficient learning system to build low-level size-invariant planners for a variety of interactive navigation problems. We evaluate on static and dynamic configurations of MazeBase grid-worlds, with randomly generated environments of several different sizes, and on a StarCraft navigation scenario, with more complex dynamics, and pixels as input.

Includes members:
  Member "the bottleneck simulator a model based deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "combining q learning and search with amortized value estimates")
  Member "cooperative motion planning for non holonomic agents with value iteration networks":
    also connected through other hyperedges:
      (none)
  Member "treeqn and atreec differentiable tree structured models for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "learning generalized reactive policies using deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transferable task execution from pixels through deep planning domain learning")
  Member "desire distant future prediction in dynamic scenes with interacting agents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "probabilistic future prediction for video scene understanding")
  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 "stabilising experience replay for deep multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi agent reinforcement learning with emergent roles")
  Member "generalized value iteration networks life beyond lattices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "value iteration networks on multiple levels of abstraction")

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)