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: "efficient safe and probably approximately complete learning of action models"
  Abstract: In this paper we explore the theoretical boundaries of planning in a setting where no model of the agent's actions is given. Instead of an action model, a set of successfully executed plans are given and the task is to generate a plan that is safe, i.e., guaranteed to achieve the goal without failing. To this end, we show how to learn a conservative model of the world in which actions are guaranteed to be applicable. This conservative model is then given to an off-the-shelf classical planner, resulting in a plan that is guaranteed to achieve the goal. However, this reduction from a model-free planning to a model-based planning is not complete: in some cases a plan will not be found even when such exists. We analyze the relation between the number of observed plans and the likelihood that our conservative approach will indeed fail to solve a solvable problem. Our analysis show that the number of trajectories needed scales gracefully.

Connected through hyperedge HE-1 (induced by source paper "learning action models from disordered and noisy plan traces"):
  with members:
    - "learning portable representations for high level planning"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "distributed representations of sentences and documents"
    - "learning strips operators from noisy and incomplete observations"
    - "new inference rules for max sat"

Connected through hyperedge HE-2 (induced by source paper "representation learning for classical planning from partially observed traces"):
  with members:
    - "learning strips action models with classical planning"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "adam a method for stochastic optimization"
    - "efficient estimation of word representations in vector space"
    - "learning symbolic models of stochastic domains"
    - "the ff planning system fast plan generation through heuristic search"

Connected through hyperedge HE-3 (induced by source paper "learning strips action models with classical planning"):
  with members:
    - "hierarchical finite state controllers for generalized planning"
    - "learning partially observable deterministic action models"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

Connected through hyperedge HE-4 (induced by source paper "learning generalized models by interrogating black box autonomous agents"):
  with members:
    - "learning strips action models with classical planning"
    - "learning partially observable deterministic action 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.AI(Artificial Intelligence)