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: "an approximation of the universal intelligence measure"
  Abstract: The Universal Intelligence Measure is a recently proposed formal definition of intelligence. It is mathematically specified, extremely general, and captures the essence of many informal definitions of intelligence. It is based on Hutter's Universal Artificial Intelligence theory, an extension of Ray Solomonoff's pioneering work on universal induction. Since the Universal Intelligence Measure is only asymptotically computable, building a practical intelligence test from it is not straightforward. This paper studies the practical issues involved in developing a real-world UIM-based performance metric. Based on our investigation, we develop a prototype implementation which we use to evaluate a number of different artificial agents.

Connected through hyperedge HE-1 (induced by source paper "intelligence via ultrafilters structural properties of some intelligence comparators of deterministic legg hutter agents"):
  with members:
    - "re evaluating evaluation"
    - "bad universal priors and notions of optimality"
    - "universal intelligence a definition of machine intelligence"

Connected through hyperedge HE-2 (induced by source paper "re evaluating evaluation"):
  with members:
    - "adversarial risk and the dangers of evaluating against weak attacks"
    - "adversarial attacks and defences competition"
    - "the mechanics of n player differentiable games"
    - "a generalised method for empirical game theoretic analysis"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "rainbow combining improvements in deep reinforcement learning"
    - "intrinsic motivation and automatic curricula via asymmetric self play"

Connected through hyperedge HE-3 (induced by source paper "why artificial intelligence needs a task theory and what it might look like"):
  with members:
    - "an overview of hierarchical task network planning"

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"
    - "non equivalence of universal priors"
    - "open problems in universal induction intelligence"
    - "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 "universal psychometrics tasks difficulty composition and decomposition"):
  with members:
    - "definition and properties to assess multi agent environments as social intelligence tests"
    - "ai evaluation past present and future"
    - "a monte carlo aixi approximation"
    - "normalized information distance"
    - "universal intelligence a definition of machine intelligence"
    - "optimal ordered problem solver"

Connected through hyperedge HE-6 (induced by source paper "ultimate intelligence part i physical completeness and objectivity of induction"):
  with members:
    - "non equivalence of universal priors"
    - "models of quantum computation and quantum programming languages"
    - "some theorems on the algorithmic approach to probability theory and information theory"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "convergence and loss bounds for bayesian sequence prediction"
    - "optimal ordered problem solver"

Connected through hyperedge HE-7 (induced by source paper "advances in artificial intelligence are you sure we are on the right track"):
  with members:
    - "a collection of definitions of intelligence"

Connected through hyperedge HE-8 (induced by source paper "ai evaluation past present and future"):
  with members:
    - "definition and properties to assess multi agent environments as social intelligence tests"
    - "the arcade learning environment an evaluation platform for general agents"
    - "no free lunch versus occam s razor in supervised learning"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "tests of machine intelligence"
    - "universal intelligence a definition of machine intelligence"
    - "graphics turing test"
    - "the 3rd international planning competition results and analysis"

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)