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 action models from disordered and noisy plan traces"
  Abstract: There is increasing awareness in the planning community that the burden of specifying complete domain models is too high, which impedes the applicability of planning technology in many real-world domains. Although there have many learning systems that help automatically learning domain models, most existing work assumes that the input traces are completely correct. A more realistic situation is that the plan traces are disordered and noisy, such as plan traces described by natural language. In this paper we propose and evaluate an approach for doing this. Our approach takes as input a set of plan traces with disordered actions and noise and outputs action models that can best explain the plan traces. We use a MAX-SAT framework for learning, where the constraints are derived from the given plan traces. Unlike traditional action models learners, the states in plan traces can be partially observable and noisy as well as the actions in plan traces can be disordered and parallel. We demonstrate the effectiveness of our approach through a systematic empirical evaluation with both IPC domains and the real-world dataset extracted from natural language documents.

Connected through hyperedge HE-1 (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"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "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"

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)