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: "a new model of plan recognition"
  Abstract: We present a new abductive, probabilistic theory of plan recognition. This model differs from previous plan recognition theories in being centered around a model of plan execution: most previous methods have been based on plans as formal objects or on rules describing the recognition process. We show that our new model accounts for phenomena omitted from most previous plan recognition theories: notably the cumulative effect of a sequence of observations of partially-ordered, interleaved plans and the effect of context on plan adoption. The model also supports inferences about the evolution of plan execution in situations where another agent intervenes in plan execution. This facility provides support for using plan recognition to build systems that will intelligently assist a user.

Connected through hyperedge HE-1 (induced by source paper "active goal recognition"):
  with members:
    - "despot online pomdp planning with regularization"
    - "the automated mapping of plans for plan recognition"
    - "online planning algorithms for pomdps"

Connected through hyperedge HE-2 (induced by source paper "generalized lr parsing and the shuffle operator"):
  with members:
    - "probabilistic state dependent grammars for plan recognition"

Connected through hyperedge HE-3 (induced by source paper "probabilistic state dependent grammars for plan recognition"):
  with members:
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "accounting for context in plan recognition with application to traffic monitoring"
    - "tractable inference for complex stochastic processes"

Connected through hyperedge HE-4 (induced by source paper "policy recognition in the abstract hidden markov model"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "the automated mapping of plans for plan recognition"
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "accounting for context in plan recognition with application to traffic monitoring"
    - "tractable inference for complex stochastic processes"
    - "probabilistic state dependent grammars for plan recognition"
    - "context specific independence in bayesian networks"
    - "dynamic network models for forecasting"

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)