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: "press space to fire automatic video game tutorial generation"
  Abstract: We propose the problem of tutorial generation for games, i.e. to generate tutorials which can teach players to play games, as an AI problem. This problem can be approached in several ways, including generating natural language descriptions of game rules, generating instructive game levels, and generating demonstrations of how to play a game using agents that play in a human-like manner. We further argue that the General Video Game AI framework provides a useful testbed for addressing this problem.

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 "automated chess commentator powered by neural chess engine"):
  with members:
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "generating levels that teach mechanics"
    - "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"
    - "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-4 (induced by source paper "organic building generation in minecraft"):
  with members:
    - "general video game rule generation"
    - "intentional computational level design"
    - "data agent"
    - "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-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"
    - "atdelfi automatically designing legible full instructions for games"
    - "generating levels that teach mechanics"
    - "the vglc the video game level corpus"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-6 (induced by source paper "general video game ai a multi track framework for evaluating agents games and content generation algorithms"):
  with members:
    - "general video game rule generation"
    - "a continuous information gain measure to find the most discriminatory problems for ai benchmarking"
    - "learning to play general video games via an object embedding network"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "the n tuple bandit evolutionary algorithm for automatic game improvement"
    - "the arcade learning environment an evaluation platform for general agents"

Connected through hyperedge HE-7 (induced by source paper "generating levels that teach mechanics"):
  with members:
    - "talakat bullet hell generation through constrained map elites"
    - "atdelfi automatically designing legible full instructions for games"
    - "deriving quests from open world mechanics"
    - "automatic mapping of nes games with mappy"
    - "the vglc the video game level corpus"

Connected through hyperedge HE-8 (induced by source paper "atdelfi automatically designing legible full instructions for games"):
  with members:
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "deriving quests from open world mechanics"
    - "automatic mapping of nes games with mappy"

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)