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: "one big net for everything"
  Abstract: I apply recent work on "learning to think" (2015) and on PowerPlay (2011) to the incremental training of an increasingly general problem solver, continually learning to solve new tasks without forgetting previous skills. The problem solver is a single recurrent neural network (or similar general purpose computer) called ONE. ONE is unusual in the sense that it is trained in various ways, e.g., by black box optimization / reinforcement learning / artificial evolution as well as supervised / unsupervised learning. For example, ONE may learn through neuroevolution to control a robot through environment-changing actions, and learn through unsupervised gradient descent to predict future inputs and vector-valued reward signals as suggested in 1990. User-given tasks can be defined through extra goal-defining input patterns, also proposed in 1990. Suppose ONE has already learned many skills. Now a copy of ONE can be re-trained to learn a new skill, e.g., through neuroevolution without a teacher. Here it may profit from re-using previously learned subroutines, but it may also forget previous skills. Then ONE is retrained in PowerPlay style (2011) on stored input/output traces of (a) ONE's copy executing the new skill and (b) previous instances of ONE whose skills are still considered worth memorizing. Simultaneously, ONE is retrained on old traces (even those of unsuccessful trials) to become a better predictor, without additional expensive interaction with the enviroment. More and more control and prediction skills are thus collapsed into ONE, like in the chunker-automatizer system of the neural history compressor (1991). This forces ONE to relate partially analogous skills (with shared algorithmic information) to each other, creating common subroutines in form of shared subnetworks of ONE, to greatly speed up subsequent learning of additional, novel but algorithmically related skills.

Connected through hyperedge HE-1 (induced by source paper "recurrent independent mechanisms"):
  with members:
    - "learning powerful policies by using consistent dynamics model"
    - "routing networks and the challenges of modular and compositional computation"
    - "reinforcement learning with competitive ensembles of information constrained primitives"
    - "a meta transfer objective for learning to disentangle causal mechanisms"
    - "modular networks learning to decompose neural computation"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"

Connected through hyperedge HE-2 (induced by source paper "self organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks"):
  with members:
    - "model based reinforcement learning for atari"
    - "learning abstract options"
    - "a novel predictive coding inspired variational rnn model for online prediction and recognition"
    - "bayesian model agnostic meta learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "probabilistic model agnostic meta learning"
    - "recurrent world models facilitate policy evolution"
    - "learning to embed probabilistic structures between deterministic chaos and random process in a variational bayes predictive coding rnn"

Connected through hyperedge HE-3 (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"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "coulomb gans provably optimal nash equilibria via potential fields"
    - "curiosity driven exploration by self supervised prediction"

Connected through hyperedge HE-4 (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"
    - "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"
    - "deep learning in neural networks"

Connected through hyperedge HE-5 (induced by source paper "recurrent world models facilitate policy evolution"):
  with members:
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "visual interaction networks"
    - "attention is all you need"
    - "unsupervised learning of disentangled representations from video"
    - "autoencoder augmented neuroevolution for visual doom playing"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "a benchmark environment motivated by industrial control problems"

Connected through hyperedge HE-6 (induced by source paper "unity a general platform for intelligent agents"):
  with members:
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "chalet cornell house agent learning environment"
    - "psychlab a psychology laboratory for deep reinforcement learning agents"
    - "hindsight experience replay"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "a distributional perspective on reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "world models"):
  with members:
    - "deep learning in neural 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)