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: "general video game rule generation"
  Abstract: We introduce the General Video Game Rule Generation problem, and the eponymous software framework which will be used in a new track of the General Video Game AI (GVGAI) competition. The problem is, given a game level as input, to generate the rules of a game that fits that level. This can be seen as the inverse of the General Video Game Level Generation problem. Conceptualizing these two problems as separate helps breaking the very hard problem of generating complete games into smaller, more manageable subproblems. The proposed framework builds on the GVGAI software and thus asks the rule generator for rules defined in the Video Game Description Language. We describe the API, and three different rule generators: a random, a constructive and a search-based generator. Early results indicate that the constructive generator generates playable and somewhat interesting game rules but has a limited expressive range, whereas the search-based generator generates remarkably diverse rulesets, but with an uneven quality.

Connected through hyperedge HE-1 (induced by source paper "mech elites illuminating the mechanic space of gvgai"):
  with members:
    - "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"
    - "press space to fire automatic video game tutorial generation"

Connected through hyperedge HE-2 (induced by source paper "controllable level blending between games using variational autoencoders"):
  with members:
    - "automatic game design via mechanic generation"
    - "tile pattern kl divergence for analysing and evolving game levels"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "explainable pcgml via game design patterns"
    - "automated game design via conceptual expansion"
    - "procedural content generation via machine learning pcgml"
    - "the vglc the video game level corpus"
    - "super mario as a string platformer level generation via lstms"

Connected through hyperedge HE-3 (induced by source paper "bootstrapping conditional gans for video game level generation"):
  with members:
    - "doom level generation using generative adversarial networks"
    - "image transformer"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "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"
    - "on convergence and stability of gans"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "procedural content generation through quality diversity"):
  with members:
    - "quality diversity through surprise"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "diverse agents for ad hoc cooperation in hanabi"
    - "intentional computational level design"
    - "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-5 (induced by source paper "ludii as a competition platform"):
  with members:
    - "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"
    - "atdelfi automatically designing legible full instructions for games"
    - "deceptive games"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-6 (induced by source paper "automatic critical mechanic discovery in video games"):
  with members:
    - "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"
    - "generating levels that teach mechanics"
    - "deriving quests from open world mechanics"

Connected through hyperedge HE-7 (induced by source paper "organic building generation in minecraft"):
  with members:
    - "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"
    - "generating levels that teach mechanics"

Connected through hyperedge HE-8 (induced by source paper "generative design in minecraft chronicle challenge"):
  with members:
    - "generative design in minecraft gdmc settlement generation competition"
    - "generative adversarial text to image synthesis"
    - "long term recurrent convolutional networks for visual recognition and description"

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)