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: "rethinking formal models of partially observable multiagent decision making"
  Abstract: Multiagent decision-making problems in partially observable environments are usually modeled as either extensive-form games (EFGs) within the game theory community or partially observable stochastic games (POSGs) within the reinforcement learning community. While most practical problems can be modeled in both formalisms, the communities using these models are mostly distinct with little sharing of ideas or advances. The last decade has seen dramatic progress in algorithms for EFGs, mainly driven by the challenge problem of poker. We have seen computational techniques achieving super-human performance, some variants of poker are essentially solved, and there are now sound local search algorithms which were previously thought impossible. While the advances have garnered attention, the fundamental advances are not yet understood outside the EFG community. This can be largely explained by the starkly different formalisms between the game theory and reinforcement learning communities and, further, by the unsuitability of the original EFG formalism to make the ideas simple and clear. This paper aims to address these hindrances, by advocating a new unifying formalism, a variant of POSGs, which we call Factored-Observation Games (FOGs). We prove that any timeable perfect-recall EFG can be efficiently modeled as a FOG as well as relating FOGs to other existing formalisms. Additionally, a FOG explicitly identifies the public and private components of observations, which is fundamental to the recent EFG breakthroughs. We conclude by presenting the two building-blocks of these breakthroughs --- counterfactual regret minimization and public state decomposition --- in the new formalism, illustrating our goal of a simpler path for sharing recent advances between game theory and reinforcement learning community.

Connected through hyperedge HE-1 (induced by source paper "openspiel a framework for reinforcement learning in games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "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"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"

Connected through hyperedge HE-2 (induced by source paper "improving policies via search in cooperative partially observable games"):
  with members:
    - "the hanabi challenge a new frontier for ai research"
    - "simplified action decoder for deep multi agent reinforcement learning"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "multi agent common knowledge reinforcement learning"
    - "distributed prioritized experience replay"
    - "evaluating and modelling hanabi playing agents"
    - "prioritized experience replay"

Connected through hyperedge HE-3 (induced by source paper "value functions for depth limited solving in imperfect information games beyond poker"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "depth limited solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "solving imperfect information games using decomposition"

Connected through hyperedge HE-4 (induced by source paper "when is truth telling favored in ai debate"):
  with members:
    - "finding generalizable evidence by learning to convince q a models"
    - "supervising strong learners by amplifying weak experts"

Connected through hyperedge HE-5 (induced by source paper "problems with the efg formalism a solution attempt using observations"):
  with members:
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "solving imperfect information games using decomposition"

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)