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: "automated game design via conceptual expansion"
  Abstract: Automated game design has remained a key challenge within the field of Game AI. In this paper, we introduce a method for recombining existing games to create new games through a process called conceptual expansion. Prior automated game design approaches have relied on hand-authored or crowd-sourced knowledge, which limits the scope and applications of such systems. Our approach instead relies on machine learning to learn approximate representations of games. Our approach recombines knowledge from these learned representations to create new games via conceptual expansion. We evaluate this approach by demonstrating the ability for the system to recreate existing games. To the best of our knowledge, this represents the first machine learning-based automated game design system.

Connected through hyperedge HE-1 (induced by source paper "conceptual game expansion"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "automatic game design via mechanic generation"
    - "monte carlo tree search for simulation based strategy analysis"
    - "world models"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "automatic mapping of nes games with mappy"
    - "procedural content generation via machine learning pcgml"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "generating interactive worlds with text"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "learning to speak and act in a fantasy text adventure game"
    - "gender bias in contextualized word embeddings"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "understanding the origins of bias in word embeddings"
    - "design design inspiration from generative networks"
    - "hierarchical neural story generation"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (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"
    - "learning to blend computer game levels"

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)