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: "intentional computational level design"
  Abstract: The procedural generation of levels and content in video games is a challenging AI problem. Often such generation relies on an intelligent way of evaluating the content being generated so that constraints are satisfied and/or objectives maximized. In this work, we address the problem of creating levels that are not only playable but also revolve around specific mechanics in the game. We use constrained evolutionary algorithms and quality-diversity algorithms to generate small sections of Super Mario Bros levels called scenes, using three different simulation approaches: Limited Agents, Punishing Model, and Mechanics Dimensions. All three approaches are able to create scenes that give opportunity for a player to encounter or use targeted mechanics with different properties. We conclude by discussing the advantages and disadvantages of each approach and compare them to each other.

Connected through hyperedge HE-1 (induced by source paper "increasing generality in machine learning through procedural content generation"):
  with members:
    - "fully differentiable procedural content generation through generative playing networks"
    - "pcgrl procedural content generation via reinforcement learning"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "procedural content generation through quality diversity"
    - "arena a general evaluation platform and building toolkit for multi agent intelligence"
    - "continual lifelong learning with neural networks a review"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-2 (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"
    - "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"
    - "press space to fire automatic video game tutorial generation"

Connected through hyperedge HE-3 (induced by source paper "mario level generation from mechanics using scene stitching"):
  with members:
    - "pcgrl procedural content generation via reinforcement learning"
    - "tile pattern kl divergence for analysing and evolving game levels"
    - "talakat bullet hell generation through constrained map elites"
    - "generating levels that teach mechanics"
    - "procedural content generation via machine learning pcgml"

Connected through hyperedge HE-4 (induced by source paper "interactive constrained map elites analysis and evaluation of the expressiveness of the feature dimensions"):
  with members:
    - "procedural content generation through quality diversity"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "data agent"
    - "talakat bullet hell generation through constrained map elites"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-5 (induced by source paper "procedural content generation from automatically generating game levels to increasing generality in machine learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "continual lifelong learning with neural networks a review"
    - "deceptionnet network driven domain randomization"
    - "meta sim learning to generate synthetic datasets"
    - "the animal ai environment training and testing animal like artificial cognition"
    - "bootstrapping conditional gans for video game level generation"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"

Connected through hyperedge HE-6 (induced by source paper "procedural content generation through quality diversity"):
  with members:
    - "general video game rule generation"
    - "quality diversity through surprise"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "adaptive music composition for games"
    - "talakat bullet hell generation through constrained map elites"
    - "deceptive games"

Connected through hyperedge HE-7 (induced by source paper "automatic critical mechanic discovery in video games"):
  with members:
    - "general video game rule generation"
    - "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"
    - "generating levels that teach mechanics"
    - "deriving quests from open world mechanics"

Connected through hyperedge HE-8 (induced by source paper "organic building generation in minecraft"):
  with members:
    - "general video game rule generation"
    - "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"
    - "generating levels that teach mechanics"

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)