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: "bootstrapping conditional gans for video game level generation"
  Abstract: Generative Adversarial Networks (GANs) have shown im-pressive results for image generation. However, GANs facechallenges in generating contents with certain types of con-straints, such as game levels. Specifically, it is difficult togenerate levels that have aesthetic appeal and are playable atthe same time. Additionally, because training data usually islimited, it is challenging to generate unique levels with cur-rent GANs. In this paper, we propose a new GAN architec-ture namedConditional Embedding Self-Attention Genera-tive Adversarial Network(CESAGAN) and a new bootstrap-ping training procedure. The CESAGAN is a modification ofthe self-attention GAN that incorporates an embedding fea-ture vector input to condition the training of the discriminatorand generator. This allows the network to model non-localdependency between game objects, and to count objects. Ad-ditionally, to reduce the number of levels necessary to trainthe GAN, we propose a bootstrapping mechanism in whichplayable generated levels are added to the training set. Theresults demonstrate that the new approach does not only gen-erate a larger number of levels that are playable but also gen-erates fewer duplicate levels compared to a standard GAN.

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 "mech elites illuminating the mechanic space of gvgai"):
  with members:
    - "general video game rule generation"
    - "procedural content generation through quality diversity"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "intentional computational level design"
    - "empowering quality diversity in dungeon design with interactive constrained map elites"
    - "deep reinforcement learning for general video game ai"
    - "talakat bullet hell generation through constrained map elites"
    - "press space to fire automatic video game tutorial generation"

Connected through hyperedge HE-3 (induced by source paper "generative adversarial network rooms in generative graph grammar dungeons for the legend of zelda"):
  with members:
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "the vglc the video game level corpus"

Connected through hyperedge HE-4 (induced by source paper "procedural content generation from automatically generating game levels to increasing generality in machine learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "continual lifelong learning with neural networks a review"
    - "deceptionnet network driven domain randomization"
    - "meta sim learning to generate synthetic datasets"
    - "the animal ai environment training and testing animal like artificial cognition"
    - "intentional computational level design"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"

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)