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 "a note about the generalisation of the c tests"):
  Abstract: In this exploratory note we ask the question of what a measure of performance for all tasks is like if we use a weighting of tasks based on a difficulty function. This difficulty function depends on the complexity of the (acceptable) solution for the task (instead of a universal distribution over tasks or an adaptive test). The resulting aggregations and decompositions are (now retrospectively) seen as the natural (and trivial) interactive generalisation of the C-tests.

Includes members:
  Member "universal psychometrics tasks difficulty composition and decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a framework for searching for general artificial intelligence")
  Member "definition and properties to assess multi agent environments as social intelligence tests":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "universal psychometrics tasks difficulty composition and decomposition")
  Member "ai evaluation past present and future":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "universal psychometrics tasks difficulty composition and decomposition")
  Member "the arcade learning environment an evaluation platform for general agents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  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 "tests of machine intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "measuring an artificial intelligence system s performance on a verbal iq test for young children")
  Member "universal intelligence a definition of machine intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")

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)