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: "the vglc the video game level corpus"
  Abstract: Levels are a key component of many different video games, and a large body of work has been produced on how to procedurally generate game levels. Recently, Machine Learning techniques have been applied to video game level generation towards the purpose of automatically generating levels that have the properties of the training corpus. Towards that end we have made available a corpora of video game levels in an easy to parse format ideal for different machine learning and other game AI research purposes.

Connected through hyperedge HE-1 (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"
    - "super mario as a string platformer level generation via lstms"

Connected through hyperedge HE-2 (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"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "intentional computational level design"):
  with members:
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "talakat bullet hell generation through constrained map elites"
    - "press space to fire automatic video game tutorial generation"
    - "atdelfi automatically designing legible full instructions for games"
    - "generating levels that teach mechanics"
    - "illuminating search spaces by mapping elites"

Connected through hyperedge HE-4 (induced by source paper "tile pattern kl divergence for analysing and evolving game levels"):
  with members:
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "procedural content generation via machine learning pcgml"

Connected through hyperedge HE-5 (induced by source paper "doom level generation using generative adversarial networks"):
  with members:
    - "2d slam quality evaluation methods"
    - "procedural content generation via machine learning pcgml"
    - "improved training of wasserstein gans"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-6 (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"
    - "super mario as a string platformer level generation via lstms"
    - "clingo asp control preliminary report"

Connected through hyperedge HE-7 (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"
    - "super mario as a string platformer level generation via lstms"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "explainable pcgml via game design patterns"):
  with members:
    - "teaching meaningful explanations"
    - "rationalization a neural machine translation approach to generating natural language explanations"
    - "procedural content generation via machine learning pcgml"
    - "super mario as a string platformer level generation via lstms"
    - "rethinking the inception architecture for computer vision"

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.HC(Human-Computer Interaction)