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: "automatic mapping of nes games with mappy"
  Abstract: Game maps are useful for human players, general-game-playing agents, and data-driven procedural content generation. These maps are generally made by hand-assembling manually-created screen-shots of game levels. Besides being tedious and error-prone, this approach requires additional effort for each new game and level to be mapped. The results can still be hard for humans or computational systems to make use of, privileging visual appearance over semantic information. We describe a software system, Mappy, that produces a good approximation of a linked map of rooms given a Nintendo Entertainment System game program and a sequence of button inputs exploring its world. In addition to visual maps, Mappy outputs grids of tiles (and how they change over time), positions of non-tile objects, clusters of similar rooms that might in fact be the same room, and a set of links between these rooms. We believe this is a necessary step towards developing larger corpora of high-quality semantically-annotated maps for PCG via machine learning and other applications.

Connected through hyperedge HE-1 (induced by source paper "conceptual game expansion"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "automatic game design via mechanic generation"
    - "monte carlo tree search for simulation based strategy analysis"
    - "world models"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "automated game design via conceptual expansion"
    - "procedural content generation via machine learning pcgml"

Connected through hyperedge HE-2 (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"
    - "generating levels that teach mechanics"

Connected through hyperedge HE-3 (induced by source paper "taking the scenic route automatic exploration for videogames"):
  with members:
    - "monte carlo tree search for simulation based strategy analysis"
    - "playing hard exploration games by watching youtube"
    - "large scale study of curiosity driven learning"
    - "planning with pixels in almost real time"
    - "automated game design learning"
    - "unifying count based exploration and intrinsic motivation"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-4 (induced by source paper "addressing the fundamental tension of pcgml with discriminative learning"):
  with members:
    - "co creative level design via machine learning"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "explainable pcgml via game design patterns"
    - "procedural content generation via machine learning pcgml"
    - "the vglc the video game level corpus"
    - "super mario as a string platformer level generation via lstms"
    - "clingo asp control preliminary report"

Connected through hyperedge HE-5 (induced by source paper "automated game design via conceptual expansion"):
  with members:
    - "general video game rule generation"
    - "automatic game design via mechanic generation"
    - "creative invention benchmark"
    - "procedural content generation via machine learning pcgml"
    - "charda causal hybrid automata recovery via dynamic analysis"
    - "automated game design learning"
    - "learning to blend computer game levels"

Connected through hyperedge HE-6 (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"
    - "atdelfi automatically designing legible full instructions for games"
    - "deriving quests from open world mechanics"
    - "the vglc the video game level corpus"

Connected through hyperedge HE-7 (induced by source paper "atdelfi automatically designing legible full instructions for games"):
  with members:
    - "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"
    - "deriving quests from open world 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)