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: "driven by compression progress a simple principle explains essential aspects of subjective beauty novelty surprise interestingness attention curiosity creativity art science music jokes"
  Abstract: I argue that data becomes temporarily interesting by itself to some self-improving, but computationally limited, subjective observer once he learns to predict or compress the data in a better way, thus making it subjectively simpler and more beautiful. Curiosity is the desire to create or discover more non-random, non-arbitrary, regular data that is novel and surprising not in the traditional sense of Boltzmann and Shannon but in the sense that it allows for compression progress because its regularity was not yet known. This drive maximizes interestingness, the first derivative of subjective beauty or compressibility, that is, the steepness of the learning curve. It motivates exploring infants, pure mathematicians, composers, artists, dancers, comedians, yourself, and (since 1990) artificial systems.

Connected through hyperedge HE-1 (induced by source paper "meta learning curiosity algorithms"):
  with members:
    - "evolving loss functions with multivariate taylor polynomial parameterizations"
    - "few shot bayesian imitation learning with logical program policies"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "self supervised exploration via disagreement"
    - "few shot bayesian imitation learning with logic over programs"
    - "discovery of useful questions as auxiliary tasks"
    - "world discovery models"
    - "weight agnostic neural networks"

Connected through hyperedge HE-2 (induced by source paper "unsupervised curricula for visual meta reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "exploration by random network distillation"
    - "unsupervised meta learning for reinforcement learning"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "deep clustering for unsupervised learning of visual features"
    - "model based active exploration"

Connected through hyperedge HE-3 (induced by source paper "adapting behaviour via intrinsic reward a survey and empirical study"):
  with members:
    - "learning feature relevance through step size adaptation in temporal difference learning"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "learning to play with intrinsically motivated self aware agents"
    - "large scale study of curiosity driven learning"
    - "curiosity driven reinforcement learning with homeostatic regulation"
    - "unsupervised learning of goal spaces for intrinsically motivated goal exploration"
    - "automated curriculum learning for neural networks"
    - "hindsight experience replay"

Connected through hyperedge HE-4 (induced by source paper "adapting behaviour for learning progress"):
  with members:
    - "experience replay optimization"
    - "on inductive biases in deep reinforcement learning"
    - "adapting behaviour via intrinsic reward a survey and empirical study"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "meta gradient reinforcement learning"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"

Connected through hyperedge HE-5 (induced by source paper "boredom driven curious learning by homeo heterostatic value gradients"):
  with members:
    - "count based exploration with neural density models"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-6 (induced by source paper "unsupervised meta learning for reinforcement learning"):
  with members:
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "efficient exploration via state marginal matching"
    - "some considerations on learning to explore via meta reinforcement learning"
    - "universal planning networks"
    - "temporal difference models model free deep rl for model based control"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "unsupervised learning via meta learning"
    - "meta reinforcement learning of structured exploration strategies"

Connected through hyperedge HE-7 (induced by source paper "learning awareness models"):
  with members:
    - "distributed prioritized experience replay"
    - "curiosity driven reinforcement learning with homeostatic regulation"
    - "parameter space noise for exploration"
    - "learning to represent haptic feedback for partially observable tasks"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "the consciousness prior"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "preparing for the unknown learning a universal policy with online system identification"

Connected through hyperedge HE-8 (induced by source paper "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"):
  with members:
    - "equivalence between policy gradients and soft q learning"
    - "meta learning shared hierarchies"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "feudal networks for hierarchical reinforcement learning"
    - "prediction and control with temporal segment models"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "reinforcement learning with deep energy based policies"
    - "stochastic neural networks for hierarchical reinforcement learning"

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)