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: "explainable pcgml via game design patterns"
  Abstract: Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is that, unlike search or grammar-based PCG, it does not require hand authoring of initial content or rules. Instead, PCGML relies on existing content and black box models, which can be difficult to tune or tweak without expert knowledge. This is especially problematic when a human designer needs to understand how to manipulate their data or models to achieve desired results. We present an approach to Explainable PCGML via Design Patterns in which the design patterns act as a vocabulary and mode of interaction between user and model. We demonstrate that our technique outperforms non-explainable versions of our system in interactions with five expert designers, four of whom lack any machine learning expertise.

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"
    - "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-2 (induced by source paper "automated rationale generation a technique for explainable ai and its effects on human perceptions"):
  with members:
    - "expressing robot incapability"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "teaching meaningful explanations"
    - "explanation in artificial intelligence insights from the social sciences"
    - "residual attention network for image classification"
    - "image captioning with semantic attention"
    - "the mythos of model interpretability"
    - "why should i trust you explaining the predictions of any classifier"

Connected through hyperedge HE-3 (induced by source paper "an interaction framework for studying co creative ai"):
  with members:
    - "friend collaborator student manager how design of an ai driven game level editor affects creators"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "co creative level design via machine learning"
    - "evaluating creativity in computational co creative systems"
    - "bringing impressionism to life with neural style transfer in come swim"
    - "event representations for automated story generation with deep neural nets"
    - "procedural content generation via machine learning pcgml"
    - "statistical parametric speech synthesis incorporating generative adversarial networks"

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"
    - "automatic mapping of nes games with mappy"
    - "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"

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)