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: "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
  Abstract: Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content Generation (PCG) of levels for video games could benefit from such models, especially for games where there is a pre-existing corpus of levels to emulate. This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus. The approach successfully generates a variety of levels similar to one in the original corpus, but is further improved by application of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Specifically, various fitness functions are used to discover levels within the latent space of the GAN that maximize desired properties. Simple static properties are optimized, such as a given distribution of tile types. Additionally, the champion A* agent from the 2009 Mario AI competition is used to assess whether a level is playable, and how many jumping actions are required to beat it. These fitness functions allow for the discovery of levels that exist within the space of examples designed by experts, and also guide the search towards levels that fulfill one or more specified objectives.

Connected through hyperedge HE-1 (induced by source paper "increasing generality in machine learning through procedural content generation"):
  with members:
    - "fully differentiable procedural content generation through generative playing networks"
    - "pcgrl procedural content generation via reinforcement learning"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "leveraging procedural generation to benchmark reinforcement learning"
    - "procedural content generation through quality diversity"
    - "arena a general evaluation platform and building toolkit for multi agent intelligence"
    - "continual lifelong learning with neural networks a review"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-2 (induced by source paper "statistical guarantees of generative adversarial networks for distribution estimation"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "improving generalization and stability of generative adversarial networks"
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "improving gan training via binarized representation entropy bre regularization"

Connected through hyperedge HE-3 (induced by source paper "fully differentiable procedural content generation through generative playing networks"):
  with members:
    - "pcgrl procedural content generation via reinforcement learning"
    - "procedural content generation from automatically generating game levels to increasing generality in machine learning"
    - "deep reinforcement learning for general video game ai"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"
    - "deep learning for video game playing"
    - "procedural content generation via machine learning pcgml"

Connected through hyperedge HE-4 (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"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "automated game design via conceptual expansion"
    - "automatic mapping of nes games with mappy"
    - "procedural content generation via machine learning pcgml"

Connected through hyperedge HE-5 (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"
    - "co creative level design via machine learning"
    - "q deckrec a fast deck recommendation system for collectible card games"
    - "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-6 (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"
    - "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"
    - "super mario as a string platformer level generation via lstms"

Connected through hyperedge HE-7 (induced by source paper "automating representation discovery with map elites"):
  with members:
    - "covariance matrix adaptation for the rapid illumination of behavior space"
    - "mapping hearthstone deck spaces through map elites with sliding boundaries"
    - "autonomous skill discovery with quality diversity and unsupervised descriptors"
    - "discovering the elite hypervolume by leveraging interspecies correlation"
    - "deep interactive evolution"
    - "isolating sources of disentanglement in variational autoencoders"
    - "evolving deep neural networks"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "generative adversarial network rooms in generative graph grammar dungeons for the legend of zelda"):
  with members:
    - "bootstrapping conditional gans for video game level generation"
    - "the vglc the video game level corpus"

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)