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: "nonparametric general reinforcement learning"
  Abstract: Reinforcement learning (RL) problems are often phrased in terms of Markov decision processes (MDPs). In this thesis we go beyond MDPs and consider RL in environments that are non-Markovian, non-ergodic and only partially observable. Our focus is not on practical algorithms, but rather on the fundamental underlying problems: How do we balance exploration and exploitation? How do we explore optimally? When is an agent optimal? We follow the nonparametric realizable paradigm. #R##N#We establish negative results on Bayesian RL agents, in particular AIXI. We show that unlucky or adversarial choices of the prior cause the agent to misbehave drastically. Therefore Legg-Hutter intelligence and balanced Pareto optimality, which depend crucially on the choice of the prior, are entirely subjective. Moreover, in the class of all computable environments every policy is Pareto optimal. This undermines all existing optimality properties for AIXI. However, there are Bayesian approaches to general RL that satisfy objective optimality guarantees: We prove that Thompson sampling is asymptotically optimal in stochastic environments in the sense that its value converges to the value of the optimal policy. We connect asymptotic optimality to regret given a recoverability assumption on the environment that allows the agent to recover from mistakes. Hence Thompson sampling achieves sublinear regret in these environments. #R##N#Our results culminate in a formal solution to the grain of truth problem: A Bayesian agent acting in a multi-agent environment learns to predict the other agents' policies if its prior assigns positive probability to them (the prior contains a grain of truth). We construct a large but limit computable class containing a grain of truth and show that agents based on Thompson sampling over this class converge to play Nash equilibria in arbitrary unknown computable multi-agent environments.

Connected through hyperedge HE-1 (induced by source paper "embedded agency"):
  with members:
    - "supervising strong learners by amplifying weak experts"
    - "categorizing variants of goodhart s law"
    - "occam s razor is insufficient to infer the preferences of irrational agents"
    - "functional decision theory a new theory of instrumental rationality"
    - "reinforcement learning with a corrupted reward channel"
    - "cooperative inverse reinforcement learning"
    - "concrete problems in ai safety"
    - "a formal solution to the grain of truth problem"

Connected through hyperedge HE-2 (induced by source paper "agi safety literature review"):
  with members:
    - "categorizing variants of goodhart s law"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "rainbow combining improvements in deep reinforcement learning"
    - "ai safety gridworlds"
    - "analyzing hidden representations in end to end automatic speech recognition systems"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "when will ai exceed human performance evidence from ai experts"
    - "deep tamer interactive agent shaping in high dimensional state spaces"

Connected through hyperedge HE-3 (induced by source paper "expanding the active inference landscape more intrinsic motivations in the perception action loop"):
  with members:
    - "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation"
    - "accelerating empowerment computation with uct tree search"
    - "universal reinforcement learning algorithms survey and experiments"
    - "minimum information lqg control part ii retentive controllers"
    - "variational intrinsic control"
    - "the umwelt of an embodied agent a measure theoretic definition"
    - "generalized thompson sampling for sequential decision making and causal inference"
    - "empowerment an introduction"

Connected through hyperedge HE-4 (induced by source paper "universal reinforcement learning algorithms survey and experiments"):
  with members:
    - "count based exploration in feature space for reinforcement learning"
    - "generalised discount functions applied to a monte carlo aimu implementation"
    - "thompson sampling is asymptotically optimal in general environments"
    - "vime variational information maximizing exploration"
    - "unifying count based exploration and intrinsic motivation"
    - "bad universal priors and notions of optimality"
    - "on the computability of solomonoff induction and knowledge seeking"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "a formal solution to the grain of truth problem"):
  with members:
    - "thompson sampling is asymptotically optimal in general environments"
    - "bad universal priors and notions of optimality"
    - "on the computability of aixi"
    - "on the computability of solomonoff induction and knowledge seeking"
    - "reflective oracles a foundation for classical game theory"
    - "open problems in universal induction intelligence"

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)