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: "on learning to think algorithmic information theory for novel combinations of reinforcement learning controllers and recurrent neural world models"
  Abstract: This paper addresses the general problem of reinforcement learning (RL) in partially observable environments. In 2013, our large RL recurrent neural networks (RNNs) learned from scratch to drive simulated cars from high-dimensional video input. However, real brains are more powerful in many ways. In particular, they learn a predictive model of their initially unknown environment, and somehow use it for abstract (e.g., hierarchical) planning and reasoning. Guided by algorithmic information theory, we describe RNN-based AIs (RNNAIs) designed to do the same. Such an RNNAI can be trained on never-ending sequences of tasks, some of them provided by the user, others invented by the RNNAI itself in a curious, playful fashion, to improve its RNN-based world model. Unlike our previous model-building RNN-based RL machines dating back to 1990, the RNNAI learns to actively query its model for abstract reasoning and planning and decision making, essentially "learning to think." The basic ideas of this report can be applied to many other cases where one RNN-like system exploits the algorithmic information content of another. They are taken from a grant proposal submitted in Fall 2014, and also explain concepts such as "mirror neurons." Experimental results will be described in separate papers.

Connected through hyperedge HE-1 (induced by source paper "hypothesis driven skill discovery for hierarchical deep reinforcement learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "monet unsupervised scene decomposition and representation"
    - "model based reinforcement learning for atari"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "multi object representation learning with iterative variational inference"
    - "exploration by random network distillation"
    - "learning real world robot policies by dreaming"
    - "relational deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "object oriented dynamics learning through multi level abstraction"):
  with members:
    - "object oriented dynamics predictor"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "relational deep reinforcement learning"
    - "deep learning for generic object detection a survey"
    - "relational inductive biases deep learning and graph networks"
    - "gotta learn fast a new benchmark for generalization in rl"
    - "contingency aware exploration in reinforcement learning"
    - "visual interaction networks"

Connected through hyperedge HE-3 (induced by source paper "learning to predict without looking ahead world models without forward prediction"):
  with members:
    - "weight agnostic neural networks"
    - "reinforcement learning for improving agent design"
    - "model based reinforcement learning for atari"
    - "videoflow a flow based generative model for video"
    - "deep neuroevolution of recurrent and discrete world models"
    - "learning latent dynamics for planning from pixels"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "lipschitz continuity in model based reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "reward tampering problems and solutions in reinforcement learning a causal influence diagram perspective"):
  with members:
    - "categorizing wireheading in partially embedded agents"
    - "model based reinforcement learning for atari"
    - "understanding agent incentives using causal influence diagrams part i single action settings"
    - "modeling agi safety frameworks with causal influence diagrams"
    - "neural predictive belief representations"
    - "agi safety literature review"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "world models"

Connected through hyperedge HE-5 (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-6 (induced by source paper "continual learning using world models for pseudo rehearsal"):
  with members:
    - "model based reinforcement learning for atari"
    - "complementary learning for overcoming catastrophic forgetting using experience replay"
    - "on first order meta learning algorithms"
    - "learning latent dynamics for planning from pixels"
    - "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"
    - "deep generative dual memory network for continual learning"
    - "recurrent world models facilitate policy evolution"
    - "continual learning with deep generative replay"

Connected through hyperedge HE-7 (induced by source paper "reinforcement learning upside down don t predict rewards just map them to actions"):
  with members:
    - "progressive reinforcement learning with distillation for multi skilled motion control"
    - "one big net for everything"
    - "hindsight experience replay"
    - "hindsight policy gradients"
    - "time contrastive networks self supervised learning from video"
    - "one shot imitation learning"
    - "learning to act by predicting the future"
    - "google s neural machine translation system bridging the gap between human and machine translation"

Connected through hyperedge HE-8 (induced by source paper "policy prediction network model free behavior policy with model based learning in continuous action space"):
  with members:
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "recurrent world models facilitate policy evolution"
    - "value prediction network"
    - "recurrent environment simulators"
    - "curiosity driven exploration by self supervised prediction"
    - "proximal policy optimization algorithms"
    - "high dimensional continuous control using generalized advantage estimation"
    - "action conditional video prediction using deep networks in atari games"

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)