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: "ai evaluation past present and future"
  Abstract: Artificial intelligence develops techniques and systems whose performance must be evaluated on a regular basis in order to certify and foster progress in the discipline. We will describe and critically assess the different ways AI systems are evaluated. We first focus on the traditional task-oriented evaluation approach. We see that black-box (behavioural evaluation) is becoming more and more common, as AI systems are becoming more complex and unpredictable. We identify three kinds of evaluation: Human discrimination, problem benchmarks and peer confrontation. We describe the limitations of the many evaluation settings and competitions in these three categories and propose several ideas for a more systematic and robust evaluation. We then focus on a less customary (and challenging) ability-oriented evaluation approach, where a system is characterised by its (cognitive) abilities, rather than by the tasks it is designed to solve. We discuss several possibilities: the adaptation of cognitive tests used for humans and animals, the development of tests derived from algorithmic information theory or more general approaches under the perspective of universal psychometrics.

Connected through hyperedge HE-1 (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"
    - "an approximation of the universal intelligence measure"
    - "a monte carlo aixi approximation"
    - "normalized information distance"
    - "universal intelligence a definition of machine intelligence"
    - "optimal ordered problem solver"

Connected through hyperedge HE-2 (induced by source paper "modeling progress in ai"):
  with members:
    - "deep learning in neural networks"
    - "measuring an artificial intelligence system s performance on a verbal iq test for young children"
    - "the lovelace 2 0 test of artificial creativity and intelligence"
    - "the arcade learning environment an evaluation platform for general agents"
    - "universal intelligence a definition of machine intelligence"

Connected through hyperedge HE-3 (induced by source paper "a note about the generalisation of the c tests"):
  with members:
    - "universal psychometrics tasks difficulty composition and decomposition"
    - "definition and properties to assess multi agent environments as social intelligence tests"
    - "the arcade learning environment an evaluation platform for general agents"
    - "an approximation of the universal intelligence measure"
    - "tests of machine intelligence"
    - "universal intelligence a definition of machine intelligence"

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)