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: "generating levels that teach mechanics"
  Abstract: The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.

Connected through hyperedge HE-1 (induced by source paper "mario level generation from mechanics using scene stitching"):
  with members:
    - "pcgrl procedural content generation via reinforcement learning"
    - "intentional computational level design"
    - "tile pattern kl divergence for analysing and evolving game levels"
    - "talakat bullet hell generation through constrained map elites"
    - "procedural content generation via machine learning pcgml"

Connected through hyperedge HE-2 (induced by source paper "automated chess commentator powered by neural chess engine"):
  with members:
    - "press space to fire automatic video game tutorial generation"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "order planning neural text generation from structured data"
    - "challenges in data to document generation"
    - "story generation from sequence of independent short descriptions"
    - "deepchess end to end deep neural network for automatic learning in chess"
    - "effective approaches to attention based neural machine translation"
    - "a diversity promoting objective function for neural conversation models"

Connected through hyperedge HE-3 (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"
    - "atdelfi automatically designing legible full instructions for games"
    - "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"
    - "deriving quests from open world mechanics"

Connected through hyperedge HE-4 (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"
    - "atdelfi automatically designing legible full instructions for games"

Connected through hyperedge HE-5 (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"
    - "atdelfi automatically designing legible full instructions for games"
    - "the vglc the video game level corpus"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-6 (induced by source paper "tree search vs optimization approaches for map generation"):
  with members:
    - "intentional computational level design"
    - "talakat bullet hell generation through constrained map elites"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"

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)