human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "controllable level blending between games using variational autoencoders"):
  Abstract: Previous work explored blending levels from existing games to create levels for a new game that mixes properties of the original games. In this paper, we use Variational Autoencoders (VAEs) for improving upon such techniques. VAEs are artificial neural networks that learn and use latent representations of datasets to generate novel outputs. We train a VAE on level data from Super Mario Bros. and Kid Icarus, enabling it to capture the latent space spanning both games. We then use this space to generate level segments that combine properties of levels from both games. Moreover, by applying evolutionary search in the latent space, we evolve level segments satisfying specific constraints. We argue that these affordances make the VAE-based approach especially suitable for co-creative level design and compare its performance with similar generative models like the GAN and the VAE-GAN.

Includes members:
  Member "general video game rule generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mech elites illuminating the mechanic space of gvgai")
  Member "automatic game design via mechanic generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "conceptual game expansion")
  Member "tile pattern kl divergence for analysing and evolving game levels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mario level generation from mechanics using scene stitching")
  Member "evolving mario levels in the latent space of a deep convolutional generative adversarial network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "increasing generality in machine learning through procedural content generation")
  Member "explainable pcgml via game design patterns":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automated rationale generation a technique for explainable ai and its effects on human perceptions")
  Member "automated game design via conceptual expansion":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "conceptual game expansion")
  Member "procedural content generation via machine learning pcgml":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "increasing generality in machine learning through procedural content generation")
  Member "the vglc the video game level corpus":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative adversarial network rooms in generative graph grammar dungeons for the legend of zelda")

Question: Please tell me which class the source paper 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.LG(Machine Learning)