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: "is there an elegant universal theory of prediction"
  Abstract: Solomonoff's inductive learning model is a powerful, universal and highly elegant theory of sequence prediction. Its critical flaw is that it is incomputable and thus cannot be used in practice. It is sometimes suggested that it may still be useful to help guide the development of very general and powerful theories of prediction which are computable. In this paper it is shown that although powerful algorithms exist, they are necessarily highly complex. This alone makes their theoretical analysis problematic, however it is further shown that beyond a moderate level of complexity the analysis runs into the deeper problem of Goedel incompleteness. This limits the power of mathematics to analyse and study prediction algorithms, and indeed intelligent systems in general.

Connected through hyperedge HE-1 (induced by source paper "prediction of infinite words with automata"):
  with members:
    - "high confidence predictions under adversarial uncertainty"

Connected through hyperedge HE-2 (induced by source paper "ethical artificial intelligence"):
  with members:
    - "logical limitations to machine ethics with consequences to lethal autonomous weapons"
    - "show and tell a neural image caption generator"
    - "playing atari with deep reinforcement learning"
    - "model based utility functions"
    - "feature reinforcement learning part i unstructured mdps"
    - "feature dynamic bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "the universe of minds"):
  with members:
    - "efficiency theory a unifying theory for information computation and intelligence"
    - "construction of an np problem with an exponential lower bound"
    - "universal intelligence a definition of machine intelligence"

Connected through hyperedge HE-4 (induced by source paper "asymptotically optimal agents"):
  with members:
    - "time consistent discounting"
    - "on the possibility of learning in reactive environments with arbitrary dependence"
    - "universal intelligence a definition of machine intelligence"
    - "self optimizing and pareto optimal policies in general environments based on bayes mixtures"

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)