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: "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

Connected through hyperedge HE-1 (induced by source paper "obstacle tower a generalization challenge in vision control and planning"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "exploration by random network distillation"
    - "playing hard exploration games by watching youtube"
    - "large scale study of curiosity driven learning"
    - "unity a general platform for intelligent agents"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "pommerman a multi agent playground"

Connected through hyperedge HE-2 (induced by source paper "leveling the playing field fairness in ai versus human game benchmarks"):
  with members:
    - "generative design in minecraft gdmc settlement generation competition"
    - "innateness alphazero and artificial intelligence"
    - "starcraft ii a new challenge for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "thinking fast and slow with deep learning and tree search"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-3 (induced by source paper "starcraft ii a new challenge for reinforcement learning"):
  with members:
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "learning macromanagement in starcraft from replays using deep learning"
    - "deep q learning from demonstrations"
    - "multiagent bidirectionally coordinated nets emergence of human level coordination in learning to play starcraft combat games"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "ms pac man versus ghost team cig 2016 competition"
    - "torchcraft a library for machine learning research on real time strategy games"

Connected through hyperedge HE-4 (induced by source paper "computability and complexity of unconventional computing devices"):
  with members:
    - "a software methodology for compiling quantum programs"
    - "a survey and discussion of memcomputing machines"
    - "polynomial time solution of prime factorization and np complete problems with digital memcomputing machines"
    - "convolutional networks for fast energy efficient neuromorphic computing"
    - "when does a physical system compute"
    - "classic nintendo games are computationally hard"
    - "why philosophers should care about computational complexity"
    - "zeno machines and hypercomputation"

Connected through hyperedge HE-5 (induced by source paper "ai researchers video games are your friends"):
  with members:
    - "the arcade learning environment an evaluation platform for general agents"
    - "universal intelligence a definition of machine intelligence"

Connected through hyperedge HE-6 (induced by source paper "the arcade learning environment an evaluation platform for general agents"):
  with members:
    - "an approximation of the universal intelligence measure"

Connected through hyperedge HE-7 (induced by source paper "an approximation of the universal intelligence measure"):
  with members:
    - "reinforcement learning via aixi approximation"
    - "a monte carlo aixi approximation"
    - "temporal difference updating without a learning rate"
    - "universal intelligence a definition of machine intelligence"
    - "towards a universal theory of artificial intelligence based on algorithmic probability and sequential decision theory"

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)