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 "measuring intelligence through games"):
  Abstract: Abstract Artiﬁcial general intelligence (AGI) refers to research aimed at tackling the full problemof artiﬁcial intelligence, that is, create truly intelligent agents. This sets it apart from mostAI research which aims at solving relatively narrow domains, such as character recognition,motion planning, or increasing player satisfaction in games. But how do we know when anagent is truly intelligent? A common point of reference in the AGI community is Legg andHutter’s formal deﬁnition of universal intelligence, which has the appeal of simplicity andgenerality but is unfortunately incomputable.Games of various kinds are commonly used as benchmarks for “narrow” AI research,as they are considered to have many important properties. We argue that many of theseproperties carry over to the testing of general intelligence as well. We then sketch how suchtesting could practically be carried out. The central part of this sketch is an extension ofuniversal intelligence to deal with ﬁnite time, and the use of sampling of the space of gamesexpressed in a suitably biased game description language.Keywords: measure of intelligence, games

Includes members:
  Member "a monte carlo aixi approximation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "influence aware memory for deep reinforcement learning")
  Member "2006 celebrating 75 years of ai history and outlook the next 25 years":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "driven by compression progress a simple principle explains essential aspects of subjective beauty novelty surprise interestingness attention curiosity creativity art science music jokes")
  Member "universal intelligence a definition of machine intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "goedel machines self referential universal problem solvers making provably optimal self improvements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "understanding agent incentives using causal influence diagrams part i single action settings")

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)