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: "monitoring teams by overhearing a multi agent plan recognition approach"
  Abstract: Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents a non-intrusive approach to monitoring by overhearing, where the monitored team's state is inferred (via plan-recognition) from team-members' routine communications, exchanged as part of their coordinated task execution, and observed (overheard) by the monitoring system. Key challenges in this approach include the demanding run-time requirements of monitoring, the scarceness of observations (increasing monitoring uncertainty), and the need to scale-up monitoring to address potentially large teams. To address these, we present a set of complementary novel techniques, exploiting knowledge of the social structures and procedures in the monitored team: (i) an efficient probabilistic plan-recognition algorithm, well-suited for processing communications as observations; (ii) an approach to exploiting knowledge of the team's social behavior to predict future observations during execution (reducing monitoring uncertainty); and (iii) monitoring algorithms that trade expressivity for scalability, representing only certain useful monitoring hypotheses, but allowing for any number of agents and their different activities to be represented in a single coherent entity. We present an empirical evaluation of these techniques, in combination and apart, in monitoring a deployed team of agents, running on machines physically distributed across the country, and engaged in complex, dynamic task execution. We also compare the performance of these techniques to human expert and novice monitors, and show that the techniques presented are capable of monitoring at human-expert levels, despite the difficulty of the task.

Connected through hyperedge HE-1 (induced by source paper "autonomous agents modelling other agents a comprehensive survey and open problems"):
  with members:
    - "reasoning about hypothetical agent behaviours and their parameters"
    - "are you doing what i think you are doing criticising uncertain agent models"
    - "e hba using action policies for expert advice and agent typification"
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "an empirical study on the practical impact of prior beliefs over policy types"
    - "heuristic online goal recognition in continuous domains"
    - "a survey of learning in multiagent environments dealing with non stationarity"
    - "belief and truth in hypothesised behaviours"

Connected through hyperedge HE-2 (induced by source paper "a flexible coupling approach to multi agent planning under incomplete information"):
  with members:
    - "the lama planner guiding cost based anytime planning with landmarks"
    - "the fast downward planning system"
    - "a framework for sequential planning in multi agent settings"
    - "vhpop versatile heuristic partial order planner"
    - "the ff planning system fast plan generation through heuristic search"
    - "partial order planning with concurrent interacting actions"
    - "towards flexible teamwork"

Connected through hyperedge HE-3 (induced by source paper "location based reasoning about complex multi agent behavior"):
  with members:
    - "relational bayesian networks"
    - "improving the accuracy and efficiency of map inference for markov logic"
    - "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service"
    - "logical hidden markov models"
    - "parameter learning of logic programs for symbolic statistical modeling"
    - "bayesian logic programs"

Connected through hyperedge HE-4 (induced by source paper "observation of large scale multi agent based simulations"):
  with members:
    - "interactive execution monitoring of agent teams"

Connected through hyperedge HE-5 (induced by source paper "representing conversations for scalable overhearing"):
  with members:
    - "extending agents by transmitting protocols in open systems"
    - "relational bayesian networks"
    - "parameter learning of logic programs for symbolic statistical modeling"
    - "statistical inference and probabilistic modelling for constraint based nlp"

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)