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: "co creative level design via machine learning"
  Abstract: Procedural Level Generation via Machine Learning (PLGML), the study of generating game levels with machine learning, has received a large amount of recent academic attention. For certain measures these approaches have shown success at replicating the quality of existing game levels. However, it is unclear the extent to which they might benefit human designers. In this paper we present a framework for co-creative level design with a PLGML agent. In support of this framework we present results from a user study and results from a comparative study of PLGML approaches.

Connected through hyperedge HE-1 (induced by source paper "machine learning based co creative design framework"):
  with members:
    - "friend collaborator student manager how design of an ai driven game level editor affects creators"
    - "toward a neuro inspired creative decoder"
    - "design design inspiration from generative networks"
    - "outfit generation and style extraction via bidirectional lstm and autoencoder"
    - "styling with attention to details"
    - "generative image inpainting with contextual attention"
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-2 (induced by source paper "pcgrl procedural content generation via reinforcement learning"):
  with members:
    - "using fractal neural networks to play simcity 1 and conway s game of life at variable scales"
    - "tree search vs optimization approaches for map generation"
    - "q deckrec a fast deck recommendation system for collectible card games"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "deep learning for video game playing"
    - "procedural content generation via machine learning pcgml"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "learning the designer s preferences to drive evolution"):
  with members:
    - "procedural content generation through quality diversity"
    - "pitako recommending game design elements in cicero"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "talakat bullet hell generation through constrained map elites"
    - "procedural content generation via machine learning pcgml"
    - "machine teaching a new paradigm for building machine learning systems"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-4 (induced by source paper "friend collaborator student manager how design of an ai driven game level editor affects creators"):
  with members:
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "teaching meaningful explanations"
    - "rationalization a neural machine translation approach to generating natural language explanations"
    - "procedural content generation via machine learning pcgml"
    - "a neural representation of sketch drawings"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "deep learning for music"
    - "text based lstm networks for automatic music composition"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning for improving agent design"):
  with members:
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "the surprising creativity of digital evolution a collection of anecdotes from the evolutionary computation and artificial life research communities"
    - "jointly learning to construct and control agents using deep reinforcement learning"
    - "procedural content generation via machine learning pcgml"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"
    - "evolving soft locomotion in aquatic and terrestrial environments effects of material properties and environmental transitions"
    - "proximal policy optimization algorithms"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "the many ai challenges of hearthstone"):
  with members:
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "evolving the hearthstone meta"
    - "improving hearthstone ai by combining mcts and supervised learning algorithms"
    - "atdelfi automatically designing legible full instructions for games"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "helping ai to play hearthstone aaia 17 data mining challenge"
    - "latent predictor networks for code generation"
    - "robots that can adapt like animals"

Connected through hyperedge HE-7 (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"
    - "explainable pcgml via game design patterns"
    - "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-8 (induced by source paper "integrating automated play in level co creation"):
  with members:
    - "friend collaborator student manager how design of an ai driven game level editor affects creators"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "procedural content generation via machine learning pcgml"

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)