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 aixi"
  Abstract: How could we solve the machine learning and the artificial intelligence problem if we had infinite computation? Solomonoff induction and the reinforcement learning agent AIXI are proposed answers to this question. Both are known to be incomputable. In this paper, we quantify this using the arithmetical hierarchy, and prove upper and corresponding lower bounds for incomputability. We show that AIXI is not limit computable, thus it cannot be approximated using finite computation. Our main result is a limit-computable {\epsilon}-optimal version of AIXI with infinite horizon that maximizes expected rewards.

Connected through hyperedge HE-1 (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 solomonoff induction and knowledge seeking"
    - "reflective oracles a foundation for classical game theory"
    - "open problems in universal induction intelligence"

Connected through hyperedge HE-2 (induced by source paper "death and suicide in universal artificial intelligence"):
  with members:
    - "a monte carlo aixi approximation"

Connected through hyperedge HE-3 (induced by source paper "bad universal priors and notions of optimality"):
  with members:
    - "optimistic agents are asymptotically optimal"
    - "asymptotically optimal agents"
    - "an approximation of the universal intelligence measure"
    - "non equivalence of universal priors"
    - "open problems in universal induction intelligence"
    - "universal intelligence a definition of machine intelligence"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"
    - "a theory of universal artificial intelligence based on algorithmic complexity"

Connected through hyperedge HE-4 (induced by source paper "on the computability of solomonoff induction and knowledge seeking"):
  with members:
    - "bad universal priors and notions of optimality"
    - "asymptotically optimal agents"
    - "non equivalence of universal priors"
    - "universal prediction of selected bits"
    - "a philosophical treatise of universal induction"
    - "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)