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: "learning strips action models with classical planning"
  Abstract: This paper presents a novel approach for learning STRIPS action models from examples that compiles this inductive learning task into a classical planning task. Interestingly, the compilation approach is flexible to different amounts of available input knowledge; the learning examples can range from a set of plans (with their corresponding initial and final states) to just a pair of initial and final states (no intermediate action or state is given). Moreover, the compilation accepts partially specified action models and it can be used to validate whether the observation of a plan execution follows a given STRIPS action model, even if this model is not fully specified.

Connected through hyperedge HE-1 (induced by source paper "strips action discovery"):
  with members:
    - "learning first order symbolic representations for planning from the structure of the state space"
    - "classical planning in deep latent space bridging the subsymbolic symbolic boundary"
    - "learning strips operators from noisy and incomplete observations"
    - "learning partially observable deterministic action models"
    - "the fast downward planning system"

Connected through hyperedge HE-2 (induced by source paper "neural symbolic descriptive action model from images the search for strips"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning action models from disordered and noisy plan traces"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "lstm based goal recognition in latent space"

Connected through hyperedge HE-3 (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"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "efficient safe and probably approximately complete learning of action models"
    - "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-4 (induced by source paper "representation learning for classical planning from partially observed traces"):
  with members:
    - "efficient safe and probably approximately complete learning of action models"
    - "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-5 (induced by source paper "learning generalized models by interrogating black box autonomous agents"):
  with members:
    - "efficient safe and probably approximately complete learning of action models"
    - "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)