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: "on the computability of solomonoff induction and knowledge seeking"
  Abstract: Solomonoff induction is held as a gold standard for learning, but it is known to be incomputable. We quantify its incomputability by placing various flavors of Solomonoff's prior M in the arithmetical hierarchy. We also derive computability bounds for knowledge-seeking agents, and give a limit-computable weakly asymptotically optimal reinforcement learning agent.

Connected through hyperedge HE-1 (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"
    - "nonparametric general reinforcement learning"
    - "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"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "a formal solution to the grain of truth problem"):
  with members:
    - "nonparametric general reinforcement learning"
    - "thompson sampling is asymptotically optimal in general environments"
    - "bad universal priors and notions of optimality"
    - "on the computability of aixi"
    - "reflective oracles a foundation for classical game theory"
    - "open problems in universal induction intelligence"

Connected through hyperedge HE-3 (induced by source paper "loss bounds and time complexity for speed priors"):
  with members:
    - "non equivalence of universal priors"
    - "a monte carlo aixi approximation"
    - "optimal ordered problem solver"

Connected through hyperedge HE-4 (induced by source paper "on the computability of aixi"):
  with members:
    - "bad universal priors and notions of optimality"
    - "non equivalence of universal priors"
    - "a philosophical treatise of universal induction"
    - "a monte carlo aixi approximation"
    - "a theory of universal artificial intelligence based on algorithmic complexity"
    - "new error bounds for solomonoff prediction"

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)