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 "universal psychometrics tasks difficulty composition and decomposition"):
  Abstract: This note revisits the concepts of task and difficulty. The notion of cognitive task and its use for the evaluation of intelligent systems is still replete with issues. The view of tasks as MDP in the context of reinforcement learning has been especially useful for the formalisation of learning tasks. However, this alternate interaction does not accommodate well for some other tasks that are usual in artificial intelligence and, most especially, in animal and human evaluation. In particular, we want to have a more general account of episodes, rewards and responses, and, most especially, the computational complexity of the algorithm behind an agent solving a task. This is crucial for the determination of the difficulty of a task as the (logarithm of the) number of computational steps required to acquire an acceptable policy for the task, which includes the exploration of policies and their verification. We introduce a notion of asynchronous-time stochastic tasks. Based on this interpretation, we can see what task difficulty is, what instance difficulty is (relative to a task) and also what task compositions and decompositions are.

Includes members:
  Member "definition and properties to assess multi agent environments as social intelligence tests":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ai evaluation past present and future")
  Member "ai evaluation past present and future":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "modeling progress in ai")
  Member "an approximation of the universal intelligence measure":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "intelligence via ultrafilters structural properties of some intelligence comparators of deterministic legg hutter agents")
  Member "a monte carlo aixi approximation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "influence aware memory for deep reinforcement learning")
  Member "normalized information distance":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "one shot induction of generalized logical concepts via human guidance")
  Member "universal intelligence a definition of machine intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "optimal ordered problem solver":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automl zero evolving machine learning algorithms from scratch")

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)