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: "simple algorithmic principles of discovery subjective beauty selective attention curiosity creativity"
  Abstract: I postulate that human or other intelligent agents function or should function as follows. They store all sensory observations as they come - the data is holy. At any time, given some agent's current coding capabilities, part of the data is compressible by a short and hopefully fast program / description / explanation / world model. In the agent's subjective eyes, such data is more regular and more "beautiful" than other data. It is well-known that knowledge of regularity and repeatability may improve the agent's ability to plan actions leading to external rewards. In absence of such rewards, however, known beauty is boring. Then "interestingness" becomes the first derivative of subjective beauty: as the learning agent improves its compression algorithm, formerly apparently random data parts become subjectively more regular and beautiful. Such progress in compressibility is measured and maximized by the curiosity drive: create action sequences that extend the observation history and yield previously unknown / unpredictable but quickly learnable algorithmic regularity. We discuss how all of the above can be naturally implemented on computers, through an extension of passive unsupervised learning to the case of active data selection: we reward a general reinforcement learner (with access to the adaptive compressor) for actions that improve the subjective compressibility of the growing data. An unusually large breakthrough in compressibility deserves the name "discovery". The "creativity" of artists, dancers, musicians, pure mathematicians can be viewed as a by-product of this principle. Several qualitative examples support this hypothesis.

Connected through hyperedge HE-1 (induced by source paper "unsupervised minimax adversarial curiosity generative adversarial networks and predictability minimization"):
  with members:
    - "learning to paint with model based deep reinforcement learning"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "exploration by random network distillation"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "one big net for everything"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "coulomb gans provably optimal nash equilibria via potential fields"

Connected through hyperedge HE-2 (induced by source paper "vime variational information maximizing exploration"):
  with members:
    - "deep exploration via bootstrapped dqn"
    - "information theoretically aided reinforcement learning for embodied agents"
    - "benchmarking deep reinforcement learning for continuous control"
    - "action conditional video prediction using deep networks in atari games"
    - "trust region policy optimization"
    - "incentivizing exploration in reinforcement learning with deep predictive models"
    - "bayesian reinforcement learning a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "towards machine intelligence"):
  with members:
    - "deep learning in neural networks"
    - "going deeper with convolutions"
    - "deep learning of representations looking forward"
    - "representation learning a review and new perspectives"
    - "modeling temporal dependencies in high dimensional sequences application to polyphonic music generation and transcription"
    - "learning representations by maximizing compression"
    - "from machine learning to machine reasoning"

Connected through hyperedge HE-4 (induced by source paper "computational curiosity a book draft"):
  with members:
    - "designing socially intelligent virtual companions"
    - "reinforcement learning a survey"
    - "active learning with statistical models"

Connected through hyperedge HE-5 (induced by source paper "one decade of universal artificial intelligence"):
  with members:
    - "can intelligence explode"
    - "principles of solomonoff induction and aixi"
    - "asymptotically optimal agents"
    - "universal learning theory"
    - "an approximation of the universal intelligence measure"
    - "axioms for rational reinforcement learning"
    - "non equivalence of universal priors"
    - "feature reinforcement learning in practice"

Connected through hyperedge HE-6 (induced by source paper "can intelligence explode"):
  with members:
    - "one decade of universal artificial intelligence"
    - "a philosophical treatise of universal induction"
    - "a complete theory of everything will be subjective"
    - "a monte carlo aixi approximation"
    - "a collection of definitions of intelligence"
    - "universal intelligence a definition of machine intelligence"
    - "new millennium ai and the convergence of history"

Connected through hyperedge HE-7 (induced by source paper "driven by compression progress a simple principle explains essential aspects of subjective beauty novelty surprise interestingness attention curiosity creativity art science music jokes"):
  with members:
    - "2006 celebrating 75 years of ai history and outlook the next 25 years"
    - "new millennium ai and the convergence of history"
    - "the new ai general sound relevant for physics"
    - "goedel machines self referential universal problem solvers making provably optimal self improvements"
    - "optimal ordered problem solver"
    - "reinforcement learning a survey"

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)