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: "generative design in minecraft gdmc settlement generation competition"
  Abstract: This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map---ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.

Connected through hyperedge HE-1 (induced by source paper "the minerl competition on sample efficient reinforcement learning using human priors"):
  with members:
    - "the multi agent reinforcement learning in malmo marlo competition"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "learning to run challenge synthesizing physiologically accurate motion using deep reinforcement learning"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "rainbow combining improvements in deep reinforcement learning"
    - "one shot visual imitation learning via meta learning"
    - "deep q learning from demonstrations"

Connected through hyperedge HE-2 (induced by source paper "leveling the playing field fairness in ai versus human game benchmarks"):
  with members:
    - "innateness alphazero and artificial intelligence"
    - "starcraft ii a new challenge for reinforcement learning"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "thinking fast and slow with deep learning and tree search"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "the arcade learning environment an evaluation platform for general agents"
    - "measuring intelligence through games"

Connected through hyperedge HE-3 (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"
    - "atdelfi automatically designing legible full instructions for games"
    - "generating levels that teach mechanics"

Connected through hyperedge HE-4 (induced by source paper "rinascimento optimising statistical forward planning agents for playing splendor"):
  with members:
    - "efficient evolutionary methods for game agent optimisation model based is best"
    - "the multi agent reinforcement learning in malmo marlo competition"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "population seeding techniques for rolling horizon evolution in general video game playing"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "generative design in minecraft chronicle challenge"):
  with members:
    - "general video game rule generation"
    - "generative adversarial text to image synthesis"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-6 (induced by source paper "automatic generation of level maps with the do what s possible representation"):
  with members:
    - "automated playtesting with procedural personas through mcts with evolved heuristics"

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)