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: "atdelfi automatically designing legible full instructions for games"
  Abstract: This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (Automatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.

Connected through hyperedge HE-1 (induced by source paper "mech elites illuminating the mechanic space of gvgai"):
  with members:
    - "general video game rule generation"
    - "procedural content generation through quality diversity"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "bootstrapping conditional gans for video game level generation"
    - "intentional computational level design"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "deep reinforcement learning for general video game ai"
    - "talakat bullet hell generation through constrained map elites"

Connected through hyperedge HE-2 (induced by source paper "bootstrapping conditional gans for video game level generation"):
  with members:
    - "general video game rule generation"
    - "doom level generation using generative adversarial networks"
    - "image transformer"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "on convergence and stability of gans"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "ludii as a competition platform"):
  with members:
    - "general video game rule generation"
    - "an empirical evaluation of two general game systems ludii and rbg"
    - "prolonets neural encoding human experts domain knowledge to warm start reinforcement learning"
    - "ludii the ludemic general game system"
    - "an overview of the ludii general game system"
    - "the 2017 aibirds competition"
    - "deceptive games"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-4 (induced by source paper "the many ai challenges of hearthstone"):
  with members:
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "evolving the hearthstone meta"
    - "co creative level design via machine learning"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "helping ai to play hearthstone aaia 17 data mining challenge"
    - "latent predictor networks for code generation"
    - "robots that can adapt like animals"

Connected through hyperedge HE-5 (induced by source paper "automatic critical mechanic discovery in video games"):
  with members:
    - "general video game rule generation"
    - "intentional computational level design"
    - "deep reinforcement learning for general video game ai"
    - "press space to fire automatic video game tutorial generation"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "generating levels that teach mechanics"
    - "deriving quests from open world mechanics"

Connected through hyperedge HE-6 (induced by source paper "organic building generation in minecraft"):
  with members:
    - "general video game rule generation"
    - "intentional computational level design"
    - "data agent"
    - "press space to fire automatic video game tutorial generation"
    - "generative design in minecraft gdmc settlement generation competition"
    - "generating levels that teach mechanics"

Connected through hyperedge HE-7 (induced by source paper "intentional computational level design"):
  with members:
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "talakat bullet hell generation through constrained map elites"
    - "press space to fire automatic video game tutorial generation"
    - "generating levels that teach mechanics"
    - "the vglc the video game level corpus"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-8 (induced by source paper "generating levels that teach mechanics"):
  with members:
    - "talakat bullet hell generation through constrained map elites"
    - "press space to fire automatic video game tutorial generation"
    - "deriving quests from open world mechanics"
    - "automatic mapping of nes games with mappy"
    - "the vglc the video game level corpus"

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)