human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "bad universal priors and notions of optimality":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligence via ultrafilters structural properties of some intelligence comparators of deterministic legg hutter agents")
  Member "on the computability of aixi":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a formal solution to the grain of truth problem")
  Member "asymptotically optimal agents":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "thompson sampling is asymptotically optimal in general environments")
  Member "non equivalence of universal priors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "loss bounds and time complexity for speed priors")
  Member "universal prediction of selected bits":
    also connected through other hyperedges:
      (none)
  Member "a philosophical treatise of universal induction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "resolving spurious correlations in causal models of environments via interventions")
  Member "a theory of universal artificial intelligence based on algorithmic complexity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "new error bounds for solomonoff prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bad universal priors and notions of optimality")

Question: Please tell me which class the source paper 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)