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: "accounting for context in plan recognition with application to traffic monitoring"
  Abstract: Typical approaches to plan recognition start from a representation of an agent's possible plans, and reason evidentially from observations of the agent's actions to assess the plausibility of the various candidates. A more expansive view of the task (consistent with some prior work) accounts for the context in which the plan was generated, the mental state and planning process of the agent, and consequences of the agent's actions in the world. We present a general Bayesian framework encompassing this view, and focus on how context can be exploited in plan recognition. We demonstrate the approach on a problem in traffic monitoring, where the objective is to induce the plan of the driver from observation of vehicle movements. Starting from a model of how the driver generates plans, we show how the highway context can appropriately influence the recognizer's interpretation of observed driver behavior.

Connected through hyperedge HE-1 (induced by source paper "landmark based approaches for goal recognition as planning"):
  with members:
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "a unified framework for planning in adversarial and cooperative environments"
    - "heuristic online goal recognition in continuous domains"
    - "session analysis using plan recognition"
    - "landmark based plan recognition"
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "the fast downward planning system"
    - "multiple goal heuristic search"

Connected through hyperedge HE-2 (induced by source paper "probabilistic state dependent grammars for plan recognition"):
  with members:
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "tractable inference for complex stochastic processes"
    - "a new model of plan recognition"

Connected through hyperedge HE-3 (induced by source paper "abstraction in belief networks the role of intermediate states in diagnostic reasoning"):
  with members:
    - "ideal a software package for analysis of influence diagrams"
    - "state space abstraction for anytime evaluation of probabilistic networks"

Connected through hyperedge HE-4 (induced by source paper "probabilistic models for agents beliefs and decisions"):
  with members:
    - "from influence diagrams to junction trees"
    - "the lumiere project bayesian user modeling for inferring the goals and needs of software users"

Connected through hyperedge HE-5 (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"
    - "tractable inference for complex stochastic processes"
    - "a new model of plan recognition"
    - "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)