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: "deep reinforcement learning for general video game ai"
  Abstract: The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward model that allows the use of algorithms such as Monte Carlo Tree Search. #R##N#In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. Using this interface, we characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of GVGAI games. We further analyze the results to provide a first indication of the relative difficulty of these games relative to each other, and relative to those in the Arcade Learning Environment under similar conditions.

Connected through hyperedge HE-1 (induced by source paper "fully differentiable procedural content generation through generative playing networks"):
  with members:
    - "pcgrl procedural content generation via reinforcement learning"
    - "procedural content generation from automatically generating game levels to increasing generality in machine learning"
    - "evolving mario levels in the latent space of a deep convolutional generative adversarial network"
    - "a study on overfitting in deep reinforcement learning"
    - "quantifying generalization in reinforcement learning"
    - "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-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"
    - "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 "rotation translation and cropping for zero shot generalization"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "generalization in reinforcement learning with selective noise injection and information bottleneck"
    - "leveraging procedural generation to benchmark 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"
    - "quantifying generalization in reinforcement learning"
    - "illuminating generalization in deep reinforcement learning through procedural level generation"

Connected through hyperedge HE-4 (induced by source paper "the chef s hat simulation environment for reinforcement learning based agents"):
  with members:
    - "it s food fight introducing the chef s hat card game for affective aware hri"
    - "qualitative measurements of policy discrepancy for return based deep q network"
    - "deep q learning from demonstrations"
    - "proximal policy optimization algorithms"
    - "extending the openai gym for robotics a toolkit for reinforcement learning using ros and gazebo"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-5 (induced by source paper "a survey of reinforcement learning informed by natural language"):
  with members:
    - "assessing bert s syntactic abilities"
    - "learning to generalize from sparse and underspecified rewards"
    - "using natural language for reward shaping in reinforcement learning"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "hierarchical decision making by generating and following natural language instructions"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "an algorithmic perspective on imitation learning"

Connected through hyperedge HE-6 (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"
    - "the n tuple bandit evolutionary algorithm for game agent optimisation"
    - "learning latent dynamics for planning from pixels"
    - "don t forget there is more than forgetting new metrics for continual learning"

Connected through hyperedge HE-7 (induced by source paper "agent modeling as auxiliary task for deep reinforcement learning"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "backplay man muss immer umkehren"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "machine theory of mind"
    - "a deep policy inference q network for multi agent systems"
    - "lenient multi agent deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "transformer based reinforcement learning for games"):
  with members:
    - "stabilizing transformers for reinforcement learning"
    - "deep reinforcement learning for sequence to sequence models"
    - "an introduction to deep reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "deep reinforcement learning a brief survey"
    - "convolutional sequence to sequence learning"
    - "long short term memory networks for machine reading"

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.LG(Machine Learning)