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: "open problems in universal induction intelligence"
  Abstract: Specialized intelligent systems can be found everywhere: finger print, handwriting, speech, and face recognition, spam filtering, chess and other game programs, robots, et al. This decade the first presumably complete mathematical theory of artificial intelligence based on universal induction-prediction-decision-action has been proposed. This information-theoretic approach solidifies the foundations of inductive inference and artificial intelligence. Getting the foundations right usually marks a significant progress and maturing of a field. The theory provides a gold standard and guidance for researchers working on intelligent algorithms. The roots of universal induction have been laid exactly half-a-century ago and the roots of universal intelligence exactly one decade ago. So it is timely to take stock of what has been achieved and what remains to be done. Since there are already good recent surveys, I describe the state-of-the-art only in passing and refer the reader to the literature. This article concentrates on the open problems in universal induction and its extension to universal intelligence.

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"
    - "on the computability of solomonoff induction and knowledge seeking"

Connected through hyperedge HE-2 (induced by source paper "forecasting using incomplete models"):
  with members:
    - "a formal approach to the problem of logical non omniscience"

Connected through hyperedge HE-3 (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"
    - "on the computability of solomonoff induction and knowledge seeking"
    - "reflective oracles a foundation for classical game theory"

Connected through hyperedge HE-4 (induced by source paper "bad universal priors and notions of optimality"):
  with members:
    - "on the computability of aixi"
    - "optimistic agents are asymptotically optimal"
    - "asymptotically optimal agents"
    - "an approximation of the universal intelligence measure"
    - "non equivalence of universal priors"
    - "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-5 (induced by source paper "a minimal active inference agent"):
  with members:
    - "a philosophical treatise of universal induction"
    - "universal algorithmic intelligence a mathematical top down approach"

Connected through hyperedge HE-6 (induced by source paper "one decade of universal artificial intelligence"):
  with members:
    - "can intelligence explode"
    - "principles of solomonoff induction and aixi"
    - "asymptotically optimal agents"
    - "universal learning theory"
    - "an approximation of the universal intelligence measure"
    - "axioms for rational reinforcement learning"
    - "non equivalence of universal priors"
    - "feature reinforcement learning in practice"

Connected through hyperedge HE-7 (induced by source paper "a philosophical treatise of universal induction"):
  with members:
    - "a complete theory of everything will be subjective"
    - "a monte carlo aixi approximation"
    - "algorithmic information theory a brief non technical guide to the field"
    - "algorithmic complexity bounds on future prediction errors"
    - "universal intelligence a definition of machine intelligence"
    - "similarity of objects and the meaning of words"
    - "adaptive online prediction by following the perturbed leader"
    - "convergence and loss bounds for bayesian sequence prediction"

Connected through hyperedge HE-8 (induced by source paper "algorithmic randomness as foundation of inductive reasoning and artificial intelligence"):
  with members:
    - "a complete theory of everything will be subjective"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "algorithmic information theory a brief non technical guide to the field"
    - "universal intelligence a definition of machine intelligence"
    - "a theory of universal artificial intelligence based on algorithmic complexity"

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)