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: "learning to blend computer game levels"
  Abstract: We present an approach to generate novel computer game levels that blend different game concepts in an unsupervised fashion. Our primary contribution is an analogical reasoning process to construct blends between level design models learned from gameplay videos. The models represent probabilistic relationships between elements in the game. An analogical reasoning process maps features between two models to produce blended models that can then generate new level chunks. As a proof-of-concept we train our system on the classic platformer game Super Mario Bros. due to its highly-regarded and well understood level design. We evaluate the extent to which the models represent stylistic level design knowledge and demonstrate the ability of our system to explain levels that were blended by human expert designers.

Connected through hyperedge HE-1 (induced by source paper "controllable level blending between games using variational autoencoders"):
  with members:
    - "general video game rule generation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "mek mechanics prototyping tool for 2d tile based turn based deterministic games"):
  with members:
    - "general video game rule generation"
    - "data driven design a case for maximalist game design"
    - "automated game design via conceptual expansion"

Connected through hyperedge HE-3 (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"
    - "automatic mapping of nes games with mappy"
    - "procedural content generation via machine learning pcgml"
    - "charda causal hybrid automata recovery via dynamic analysis"
    - "automated game design learning"

Connected through hyperedge HE-4 (induced by source paper "creative invention benchmark"):
  with members:
    - "the macgyver test a framework for evaluating machine resourcefulness and creative problem solving"
    - "the lovelace 2 0 test of artificial creativity and intelligence"

Connected through hyperedge HE-5 (induced by source paper "procedural content generation via machine learning pcgml"):
  with members:
    - "sampling generative networks"
    - "cnn based texture synthesize with semantic segment"
    - "emoji2vec learning emoji representations from their description"
    - "image to image translation with conditional adversarial networks"
    - "super mario as a string platformer level generation via lstms"
    - "a neural algorithm of artistic style"
    - "deep learning in neural networks"
    - "draw a recurrent neural network for image generation"

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)