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 "policy recognition in the abstract hidden markov model"):
  Abstract: In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

Includes members:
  Member "stochastic simulation algorithms for dynamic probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "factored probabilistic belief tracking")
  Member "the automated mapping of plans for plan recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "active goal recognition")
  Member "a computational scheme for reasoning in dynamic probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning the structure of dynamic probabilistic networks")
  Member "accounting for context in plan recognition with application to traffic monitoring":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "landmark based approaches for goal recognition as planning")
  Member "tractable inference for complex stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation")
  Member "a new model of plan recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "active goal recognition")
  Member "probabilistic state dependent grammars for plan recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "autonomous agents modelling other agents a comprehensive survey and open problems")
  Member "context specific independence in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "abstracting probabilistic models a logical perspective")

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)