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: "procedural content generation from automatically generating game levels to increasing generality in machine learning"
  Abstract: The idea behind procedural content generation (PCG) in games is to create content automatically, using algorithms, instead of relying on user-designed content. While PCG approaches have traditionally focused on creating content for video games, they are now being applied to all kinds of virtual environments, thereby enabling training of machine learning systems that are significantly more general. For example, PCG's ability to generate never-ending streams of new levels has allowed DeepMind's Capture the Flag agent to reach beyond human-level-performance. Additionally, PCG-inspired methods such as domain randomization enabled OpenAI's robot arm to learn to manipulate objects with unprecedented dexterity. Level generation in 2D arcade games has also illuminated some shortcomings of standard deep RL methods, suggesting potential ways to train more general policies. This Review looks at key aspect of PCG approaches, including its ability to (1) enable new video games (such as No Man's Sky), (2) create open-ended learning environments, (3) combat overfitting in supervised and reinforcement learning tasks, and (4) create better benchmarks that could ultimately spur the development of better learning algorithms. We hope this article can introduce the broader machine learning community to PCG, which we believe will be a critical tool in creating a more general machine intelligence.

Connected through hyperedge HE-1 (induced by source paper "automatic curriculum learning for deep rl a short survey"):
  with members:
    - "language as a cognitive tool to imagine goals in curiosity driven exploration"
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "emergent tool use from multi agent autocurricula"
    - "distilling policy distillation"
    - "language grounding through social interactions and curiosity driven multi goal learning"
    - "automated curricula through setter solver interactions"
    - "continual learning for robotics"

Connected through hyperedge HE-2 (induced by source paper "fully differentiable procedural content generation through generative playing networks"):
  with members:
    - "pcgrl procedural content generation via reinforcement learning"
    - "deep reinforcement learning for general video game ai"
    - "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-3 (induced by source paper "adapting to unseen environments through explicit representation of context"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"
    - "meta learning by the baldwin effect"
    - "learning curriculum policies for reinforcement learning"
    - "automated curriculum learning by rewarding temporally rare events"
    - "lstm a search space odyssey"
    - "schema networks zero shot transfer with a generative causal model of intuitive physics"
    - "model agnostic meta learning for fast adaptation of deep networks"

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)