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: "autonomous agents modelling other agents a comprehensive survey and open problems"
  Abstract: Abstract   Much research in artificial intelligence is concerned with the development of autonomous agents that can interact effectively with other agents. An important aspect of such agents is the ability to reason about the behaviours of other agents, by constructing  models  which make predictions about various properties of interest (such as actions, goals, beliefs) of the modelled agents. A variety of modelling approaches now exist which vary widely in their methodology and underlying assumptions, catering to the needs of the different sub-communities within which they were developed and reflecting the different practical uses for which they are intended. The purpose of the present article is to provide a comprehensive survey of the salient modelling methods which can be found in the literature. The article concludes with a discussion of open problems which may form the basis for fruitful future research.

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"
    - "evolving agents for the hanabi 2018 cig competition"
    - "learning policy representations in multiagent systems"

Connected through hyperedge HE-2 (induced by source paper "multi agent interactions modeling with correlated policies"):
  with members:
    - "a regularized opponent model with maximum entropy objective"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "multi agent adversarial inverse reinforcement learning"
    - "learning policy representations in multiagent systems"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"
    - "multi agent imitation learning for driving simulation"

Connected through hyperedge HE-3 (induced by source paper "adversarial machine learning perspectives from adversarial risk analysis"):
  with members:
    - "estimating counterfactual treatment outcomes over time through adversarially balanced representations"
    - "gradient methods for solving stackelberg games"
    - "opponent aware reinforcement learning"
    - "knowing when to stop evaluation and verification of conformity to output size specifications"
    - "gltr statistical detection and visualization of generated text"
    - "a survey of game theoretic approaches for adversarial machine learning in cybersecurity tasks"
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-4 (induced by source paper "a two stage optimization approach to safe by design planning for autonomous driving"):
  with members:
    - "parot a practical framework for robust deep neural network training"
    - "reinforcement learning for temporal logic control synthesis with probabilistic satisfaction guarantees"
    - "formal specification and verification of autonomous robotic systems a survey"
    - "learning to drive from simulation without real world labels"
    - "end to end learning for self driving cars"
    - "concrete problems in ai safety"
    - "towards verified artificial intelligence"
    - "safety verification of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "integrating planning and interpretable goal recognition for autonomous driving"):
  with members:
    - "when humans aren t optimal robots that collaborate with risk aware humans"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "energy based continuous inverse optimal control"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "belief and truth in hypothesised behaviours"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "efficient planning under uncertainty with macro actions"

Connected through hyperedge HE-6 (induced by source paper "decentralized mcts via learned teammate models"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "decentralized cooperative planning for automated vehicles with hierarchical monte carlo tree search"
    - "counterfactual multi agent policy gradients"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "i feel i feel you a theory of mind experiment in games"):
  with members:
    - "machine theory of mind"

Connected through hyperedge HE-8 (induced by source paper "multi objective multi agent decision making a utility based analysis and survey"):
  with members:
    - "emergent coordination through competition"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "resource abstraction for reinforcement learning in multiagent congestion problems"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "dynamic weights in multi objective deep reinforcement learning"
    - "ordered preference elicitation strategies for supporting multi objective decision making"

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)