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: "general video game ai a multi track framework for evaluating agents games and content generation algorithms"
  Abstract: General Video Game Playing (GVGP) aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, The General Video Game AI (GVGAI) competition framework was created and released with the purpose of providing researchers a common open-source and easy to use platform for testing their AI methods with potentially infinity of games created using Video Game Description Language (VGDL). The framework has been expanded into several tracks during the last few years to meet the demand of different research directions. The agents are required to either play multiples unknown games with or without access to game simulations, or to design new game levels or rules. This survey paper presents the VGDL, the GVGAI framework, existing tracks, and reviews the wide use of GVGAI framework in research, education and competitions five years after its birth. A future plan of framework improvements is also described.

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"
    - "bootstrapping conditional gans for video game level generation"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "project thyia a forever gameplayer"):
  with members:
    - "efficient evolutionary methods for game agent optimisation model based is best"
    - "go explore a new approach for hard exploration problems"
    - "continual lifelong learning with neural networks a review"
    - "rinascimento optimising statistical forward planning agents for playing splendor"
    - "a local approach to forward model learning results on the game of life game"
    - "deep reinforcement learning for general video game ai"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "learning latent dynamics for planning from pixels"

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"
    - "bootstrapping conditional gans for video game level generation"
    - "intentional computational level design"

Connected through hyperedge HE-5 (induced by source paper "on the measure of intelligence"):
  with members:
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "behaviour suite for reinforcement learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "leveraging procedural generation to benchmark reinforcement learning"):
  with members:
    - "behaviour suite for reinforcement learning"
    - "the animal ai environment training and testing animal like artificial cognition"
    - "network randomization a simple technique for generalization in deep reinforcement learning"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "natural environment benchmarks for reinforcement learning"
    - "generalization and regularization in dqn"
    - "a study on overfitting in deep reinforcement learning"

Connected through hyperedge HE-7 (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"
    - "automated playtesting with procedural personas through mcts with evolved heuristics"
    - "on convergence and stability of gans"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-8 (induced by source paper "the measure of intelligence"):
  with members:
    - "behaviour suite for reinforcement learning"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "obstacle tower a generalization challenge in vision control and planning"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "quantifying generalization in reinforcement learning"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "deep learning a critical appraisal"

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)