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: "interactive pomdp lite towards practical planning to predict and exploit intentions for interacting with self interested agents"
  Abstract: A key challenge in non-cooperative multi-agent systems is that of developing efficient planning algorithms for intelligent agents to interact and perform effectively among boundedly rational, self-interested agents (e.g., humans). The practicality of existing works addressing this challenge is being undermined due to either the restrictive assumptions of the other agents' behavior, the failure in accounting for their rationality, or the prohibitively expensive cost of modeling and predicting their intentions. To boost the practicality of research in this field, we investigate how intention prediction can be efficiently exploited and made practical in planning, thereby leading to efficient intention-aware planning frameworks capable of predicting the intentions of other agents and acting optimally with respect to their predicted intentions. We show that the performance losses incurred by the resulting planning policies are linearly bounded by the error of intention prediction. Empirical evaluations through a series of stochastic games demonstrate that our policies can achieve better and more robust performance than the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "interactive decision making for autonomous vehicles in dense traffic"):
  with members:
    - "safe reinforcement learning on autonomous vehicles"
    - "interaction aware multi agent reinforcement learning for mobile agents with individual goals"
    - "adaptive game theoretic decision making for autonomous vehicle control at roundabouts"
    - "counterfactual multi agent policy gradients"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "transferring autonomous driving knowledge on simulated and real intersections"
    - "multi agent actor critic for mixed cooperative competitive environments"

Connected through hyperedge HE-2 (induced by source paper "scalable decision theoretic planning in open and typed multiagent systems"):
  with members:
    - "online planning algorithms for pomdps"
    - "analysis of dynamic task allocation in multi robot systems"

Connected through hyperedge HE-3 (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-4 (induced by source paper "near optimal adversarial policy switching for decentralized asynchronous multi agent systems"):
  with members:
    - "ssd single shot multibox detector"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"
    - "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"
    - "the complexity of decentralized control of markov decision processes"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"
    - "a framework for sequential planning in multi agent settings"

Connected through hyperedge HE-5 (induced by source paper "individual planning in agent populations exploiting anonymity and frame action hypergraphs"):
  with members:
    - "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams"
    - "graphical models for game theory"
    - "context specific independence in bayesian networks"
    - "temporal action graph games a new representation for dynamic games"
    - "learning from humans as an i pomdp"
    - "a framework for sequential planning in multi agent settings"

Connected through hyperedge HE-6 (induced by source paper "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams"):
  with members:
    - "learning finite state controllers for partially observable environments"
    - "exploiting model equivalences for solving interactive dynamic influence diagrams"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "the communicative multiagent team decision problem analyzing teamwork theories and models"

Connected through hyperedge HE-7 (induced by source paper "a general framework for interacting bayes optimally with self interested agents using arbitrary parametric model and model prior"):
  with members:
    - "information theoretic approach to efficient adaptive path planning for mobile robotic environmental sensing"
    - "multi robot informative path planning for active sensing of environmental phenomena a tale of two algorithms"
    - "near optimal brl using optimistic local transitions"
    - "learning is planning near bayes optimal reinforcement learning via monte carlo tree search"
    - "decentralized data fusion and active sensing with mobile sensors for modeling and predicting spatiotemporal traffic phenomena"
    - "decision theoretic coordination and control for active multi camera surveillance in uncertain partially observable environments"
    - "active markov information theoretic path planning for robotic environmental sensing"

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)