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 "one decade of universal artificial intelligence"):
  Abstract: The first decade of this century has seen the nascency of the first mathematical theory of general artificial intelligence. This theory of Universal Artificial Intelligence (UAI) has made significant contributions to many theoretical, philosophical, and practical AI questions. In a series of papers culminating in book (Hutter, 2005), an exciting sound and complete mathematical model for a super intelligent agent (AIXI) has been developed and rigorously analyzed. While nowadays most AI researchers avoid discussing intelligence, the awardwinning PhD thesis (Legg, 2008) provided the philosophical embedding and investigated the UAI-based universal measure of rational intelligence, which is formal, objective and non-anthropocentric. Recently, effective approximations of AIXI have been derived and experimentally investigated in JAIR paper (Veness et al. 2011). This practical breakthrough has resulted in some impressive applications, finally muting earlier critique that UAI is only a theory. For the first time, without providing any domain knowledge, the same agent is able to self-adapt to a diverse range of interactive environments. For instance, AIXI is able to learn from scratch to play TicTacToe, Pacman, Kuhn Poker, and other games by trial and error, without even providing the rules of the games. These achievements give new hope that the grand goal of Artificial General Intelligence is not elusive. This article provides an informal overview of UAI in context. It attempts to gently introduce a very theoretical, formal, and mathematical subject, and discusses philosophical and technical ingredients, traits of intelligence, some social questions, and the past and future of UAI.

Includes members:
  Member "can intelligence explode":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "measuring the intelligence of an idealized mechanical knowing agent")
  Member "principles of solomonoff induction and aixi":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "categorizing wireheading in partially embedded agents")
  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 "universal learning theory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "falsifiable implies learnable")
  Member "an approximation of the universal intelligence measure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "intelligence via ultrafilters structural properties of some intelligence comparators of deterministic legg hutter agents")
  Member "axioms for rational reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "principles of solomonoff induction and aixi")
  Member "non equivalence of universal priors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "loss bounds and time complexity for speed priors")
  Member "feature reinforcement learning in practice":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "projective simulation with generalization")

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)