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: "deep q learning from demonstrations"
  Abstract: Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator's actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD's performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for autonomous driving a survey"):
  with members:
    - "yes we gan applying adversarial techniques for autonomous driving"
    - "multi objective multi agent decision making a utility based analysis and survey"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "on first order meta learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "go explore a new approach for hard exploration problems"
    - "skew fit state covering self supervised reinforcement learning"
    - "dynamics aware unsupervised discovery of skills"
    - "self supervised learning of image embedding for continuous control"
    - "momentum contrast for unsupervised visual representation learning"
    - "learning latent plans from play"

Connected through hyperedge HE-3 (induced by source paper "exploration efficient deep reinforcement learning with demonstration guidance for robot control"):
  with members:
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "soft actor critic algorithms and applications"
    - "addressing function approximation error in actor critic methods"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "robust adversarial reinforcement learning"
    - "deeply aggrevated differentiable imitation learning for sequential prediction"

Connected through hyperedge HE-4 (induced by source paper "periodic intra ensemble knowledge distillation for reinforcement learning"):
  with members:
    - "population guided parallel policy search for reinforcement learning"
    - "information asymmetry in kl regularized rl"
    - "knowledge distillation by on the fly native ensemble"
    - "off policy deep reinforcement learning without exploration"
    - "evolution guided policy gradient in reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "learning to walk via deep reinforcement learning"
    - "deep mutual learning"

Connected through hyperedge HE-5 (induced by source paper "on simple reactive neural networks for behaviour based reinforcement learning"):
  with members:
    - "vid2param online system identification from video for robotics applications"
    - "composable deep reinforcement learning for robotic manipulation"
    - "silhonet an rgb method for 6d object pose estimation"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "investigating human priors for playing video games"
    - "assessing generalization in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "learning to walk via deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "sample efficient reinforcement learning through learning from demonstrations in minecraft"):
  with members:
    - "minerl a large scale dataset of minecraft demonstrations"
    - "the minerl competition on sample efficient reinforcement learning using human priors"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "pre training neural networks with human demonstrations for deep reinforcement learning"
    - "reinforcement learning from imperfect demonstrations"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "experience replay for continual learning"
    - "self imitation learning"

Connected through hyperedge HE-7 (induced by source paper "multiplicative controller fusion a hybrid navigation strategy for deployment in unknown environments"):
  with members:
    - "policies modulating trajectory generators"
    - "residual reactive navigation combining classical and learned navigation strategies for deployment in unknown environments"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "on evaluation of embodied navigation agents"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "learning with training wheels speeding up training with a simple controller for deep reinforcement learning"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"

Connected through hyperedge HE-8 (induced by source paper "the chef s hat simulation environment for reinforcement learning based agents"):
  with members:
    - "it s food fight introducing the chef s hat card game for affective aware hri"
    - "deep reinforcement learning for general video game ai"
    - "qualitative measurements of policy discrepancy for return based deep q network"
    - "proximal policy optimization algorithms"
    - "extending the openai gym for robotics a toolkit for reinforcement learning using ros and gazebo"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q 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)