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: "super mario as a string platformer level generation via lstms"
  Abstract: The procedural generation of video game levels has existed for at least 30 years, but only recently have machine learning approaches been used to generate levels without specifying the rules for generation. A number of these have looked at platformer levels as a sequence of characters and performed generation using Markov chains. In this paper we examine the use of Long Short-Term Memory recurrent neural networks (LSTMs) for the purpose of generating levels trained from a corpus of Super Mario Brothers levels. We analyze a number of different data representations and how the generated levels fit into the space of human authored Super Mario Brothers levels.

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 "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"
    - "automated game design via conceptual expansion"
    - "automatic mapping of nes games with mappy"

Connected through hyperedge HE-3 (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"
    - "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"

Connected through hyperedge HE-4 (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-5 (induced by source paper "bootstrapping conditional gans for video game level generation"):
  with members:
    - "general video game rule generation"
    - "doom level generation using generative adversarial networks"
    - "image transformer"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "atdelfi automatically designing legible full instructions for games"
    - "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "on convergence and stability of gans"

Connected through hyperedge HE-6 (induced by source paper "friend collaborator student manager how design of an ai driven game level editor affects creators"):
  with members:
    - "co creative level design via machine learning"
    - "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"

Connected through hyperedge HE-7 (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"
    - "explainable pcgml via game design patterns"
    - "automatic mapping of nes games with mappy"
    - "procedural content generation via machine learning pcgml"
    - "the vglc the video game level corpus"
    - "clingo asp control preliminary report"

Connected through hyperedge HE-8 (induced by source paper "evolving mario levels in the latent space of a deep convolutional generative adversarial network"):
  with members:
    - "deep interactive evolution"
    - "procedural content generation via machine learning pcgml"
    - "improved training of wasserstein gans"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "the vglc the video game level corpus"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

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.NE(Neural and Evolutionary Computing)