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: "modeling others using oneself in multi agent reinforcement learning"
  Abstract: We consider the multi-agent reinforcement learning setting with imperfect information in which each agent is trying to maximize its own utility. The reward function depends on the hidden state (or goal) of both agents, so the agents must infer the other players' hidden goals from their observed behavior in order to solve the tasks. We propose a new approach for learning in these domains: Self Other-Modeling (SOM), in which an agent uses its own policy to predict the other agent's actions and update its belief of their hidden state in an online manner. We evaluate this approach on three different tasks and show that the agents are able to learn better policies using their estimate of the other players' hidden states, in both cooperative and adversarial settings.

Connected through hyperedge HE-1 (induced by source paper "cluster based social reinforcement learning"):
  with members:
    - "message dropout an efficient training method for multi agent deep reinforcement learning"
    - "emergent coordination through competition"
    - "personalization of health interventions using cluster based reinforcement learning"
    - "negative update intervals in deep multi agent reinforcement learning"
    - "model ensemble trust region policy optimization"
    - "inequity aversion improves cooperation in intertemporal social dilemmas"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "learning policy representations in multiagent systems"

Connected through hyperedge HE-2 (induced by source paper "rl agents implicitly learning human preferences"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "agent modeling as auxiliary task for deep reinforcement learning"
    - "machine theory of mind"
    - "visualizing and understanding atari agents"
    - "graying the black box understanding dqns"

Connected through hyperedge HE-3 (induced by source paper "a new framework for multi agent reinforcement learning centralized training and exploration with decentralized execution via policy distillation"):
  with members:
    - "a survey and critique of multiagent deep reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "actor attention critic for multi agent reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "counterfactual multi agent policy gradients"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "lenient multi agent deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "multi turn beam search for neural dialogue modeling"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "pyro deep universal probabilistic programming"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "world models"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "importance of a search strategy in neural dialogue modelling"

Connected through hyperedge HE-5 (induced by source paper "on memory mechanism in multi agent reinforcement learning"):
  with members:
    - "r maddpg for partially observable environments and limited communication"
    - "on the pitfalls of measuring emergent communication"
    - "bayes tomop a fast detection and best response algorithm towards sophisticated opponents"
    - "compositional obverter communication learning from raw visual input"
    - "emergence of communication in an interactive world with consistent speakers"
    - "machine theory of mind"
    - "emergent communication in a multi modal multi step referential game"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-6 (induced by source paper "openspiel a framework for reinforcement learning in games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "rethinking formal models of partially observable multiagent decision making"
    - "behaviour suite for reinforcement learning"
    - "computing approximate equilibria in sequential adversarial games by exploitability descent"
    - "open ended learning in symmetric zero sum games"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"

Connected through hyperedge HE-7 (induced by source paper "multi agent deep reinforcement learning with adaptive policies"):
  with members:
    - "dealing with non stationarity in multi agent deep reinforcement learning"
    - "learning when to communicate at scale in multiagent cooperative and competitive tasks"
    - "flow a modular learning framework for autonomy in traffic"
    - "counterfactual multi agent policy gradients"
    - "learning with opponent learning awareness"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "emergent complexity via multi agent competition"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"

Connected through hyperedge HE-8 (induced by source paper "agent modeling as auxiliary task for deep reinforcement learning"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "backplay man muss immer umkehren"
    - "deep reinforcement learning for general video game ai"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "machine theory of mind"
    - "a deep policy inference q network for multi agent systems"
    - "lenient multi agent deep reinforcement learning"

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)