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 framework for sequential planning in multi agent settings"
  Abstract: This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state space. Agents maintain beliefs over physical states of the environment and over models of other agents, and they use Bayesian updates to maintain their beliefs over time. The solutions map belief states to actions. Models of other agents may include their belief states and are related to agent types considered in games of incomplete information. We express the agents' autonomy by postulating that their models are not directly manipulable or observable by other agents. We show that important properties of POMDPs, such as convergence of value iteration, the rate of convergence, and piece-wise linearity and convexity of the value functions carry over to our framework. Our approach complements a more traditional approach to interactive settings which uses Nash equilibria as a solution paradigm. We seek to avoid some of the drawbacks of equilibria which may be non-unique and do not capture off-equilibrium behaviors. We do so at the cost of having to represent, process and continuously revise models of other agents. Since the agent's beliefs may be arbitrarily nested, the optimal solutions to decision making problems are only asymptotically computable. However, approximate belief updates and approximately optimal plans are computable. We illustrate our framework using a simple application domain, and we show examples of belief updates and value functions.

Connected through hyperedge HE-1 (induced by source paper "the hanabi challenge a new frontier for ai research"):
  with members:
    - "re determinizing information set monte carlo tree search in hanabi"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "dopamine a research framework for deep reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "evolving agents for the hanabi 2018 cig competition"

Connected through hyperedge HE-2 (induced by source paper "probabilistic recursive reasoning for multi agent reinforcement learning"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "the mechanics of n player differentiable games"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "machine theory of mind"
    - "multiagent soft q learning"
    - "counterfactual multi agent policy gradients"
    - "dual supervised learning"
    - "learning with opponent learning awareness"

Connected through hyperedge HE-3 (induced by source paper "building second order mental models for human robot interaction"):
  with members:
    - "on the utility of model learning in hri"
    - "bayes tomop a fast detection and best response algorithm towards sophisticated opponents"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "modelling user s theory of ai s mind in interactive intelligent systems"
    - "machine theory of mind"
    - "where do you think you re going inferring beliefs about dynamics from behavior"
    - "towards efficient detection and optimal response against sophisticated opponents"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"

Connected through hyperedge HE-4 (induced by source paper "lie on the fly strategic voting in an iterative preference elicitation process"):
  with members:
    - "cognitive hierarchy and voting manipulation"
    - "online prediction with selfish experts"
    - "a local dominance theory of voting equilibria"
    - "the computational impact of partial votes on strategic voting"
    - "crowdsourced judgement elicitation with endogenous proficiency"
    - "determining possible and necessary winners under common voting rules given partial orders"
    - "dominating manipulations in voting with partial information"
    - "equilibria of plurality voting with abstentions"

Connected through hyperedge HE-5 (induced by source paper "multi agent generalized recursive reasoning"):
  with members:
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "machine theory of mind"
    - "multiagent soft q learning"
    - "reinforcement learning with deep energy based policies"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "density estimation using real nvp"

Connected through hyperedge HE-6 (induced by source paper "opponent aware reinforcement learning"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "configurable markov decision processes"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "ai safety gridworlds"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "learning with opponent learning awareness"
    - "a unified game theoretic approach to multiagent reinforcement learning"

Connected through hyperedge HE-7 (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-8 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters"):
  with members:
    - "on convergence and optimality of best response learning with policy types in multiagent systems"
    - "belief and truth in hypothesised behaviours"
    - "exploiting causality for selective belief filtering in dynamic bayesian networks"
    - "a game theoretic model and best response learning method for ad hoc coordination in multiagent systems"
    - "team behavior in interactive dynamic influence diagrams with applications to ad hoc teams"
    - "bayesopt a bayesian optimization library for nonlinear optimization experimental design and bandits"
    - "tractable inference for complex stochastic processes"
    - "the factored frontier algorithm for approximate inference in dbns"

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)