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: "recurrent reinforcement learning a hybrid approach"
  Abstract: Successful applications of reinforcement learning in real-world problems often require dealing with partially observable states. It is in general very challenging to construct and infer hidden states as they often depend on the agent's entire interaction history and may require substantial domain knowledge. In this work, we investigate a deep-learning approach to learning the representation of states in partially observable tasks, with minimal prior knowledge of the domain. In particular, we propose a new family of hybrid models that combines the strength of both supervised learning (SL) and reinforcement learning (RL), trained in a joint fashion: The SL component can be a recurrent neural networks (RNN) or its long short-term memory (LSTM) version, which is equipped with the desired property of being able to capture long-term dependency on history, thus providing an effective way of learning the representation of hidden states. The RL component is a deep Q-network (DQN) that learns to optimize the control for maximizing long-term rewards. Extensive experiments in a direct mailing campaign problem demonstrate the effectiveness and advantages of the proposed approach, which performs the best among a set of previous state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "shaping belief states with generative environment models for rl"):
  with members:
    - "conditional adversarial generative flow for controllable image synthesis"
    - "preventing posterior collapse with delta vaes"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "learning attractor dynamics for generative memory"
    - "geometric consistency for self supervised end to end visual odometry"
    - "learning and querying fast generative models for reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "air learning an ai research platform for algorithm hardware benchmarking of autonomous aerial robots"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "mavbench micro aerial vehicle benchmarking"
    - "deep reinforcement learning for swarm systems"
    - "deep drone racing learning agile flight in dynamic environments"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning for uav attitude control"
    - "learning navigation behaviors end to end with autorl"
    - "learning transferable architectures for scalable image recognition"

Connected through hyperedge HE-3 (induced by source paper "causally correct partial models for reinforcement learning"):
  with members:
    - "causal discovery with reinforcement learning"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "combating the compounding error problem with a multi step model"
    - "model based reinforcement learning for atari"
    - "an investigation of model free planning"
    - "when to trust your model model based policy optimization"
    - "shaping belief states with generative environment models for rl"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-4 (induced by source paper "machine reading comprehension for answer re ranking in customer support chatbots"):
  with members:
    - "semeval 2017 task 3 community question answering"
    - "towards automated customer support"
    - "deep contextualized word representations"
    - "coqa a conversational question answering challenge"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "deeprank a new deep architecture for relevance ranking in information retrieval"
    - "attention is all you need"
    - "towards an automatic turing test learning to evaluate dialogue responses"

Connected through hyperedge HE-5 (induced by source paper "high dimensional control using generalized auxiliary tasks"):
  with members:
    - "samples are not all useful denoising policy gradient updates using variance"
    - "terminal prediction as an auxiliary task for deep reinforcement learning"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "recurrent world models facilitate policy evolution"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"

Connected through hyperedge HE-6 (induced by source paper "merl multi head reinforcement learning"):
  with members:
    - "samples are useful not always denoising policy gradient updates using variance explained"
    - "terminal prediction as an auxiliary task for deep reinforcement learning"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "state representation learning for control an overview"
    - "transparency and explanation in deep reinforcement learning neural networks"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"

Connected through hyperedge HE-7 (induced by source paper "an introduction to deep reinforcement learning"):
  with members:
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "learning to play with intrinsically motivated self aware agents"
    - "horizon facebook s open source applied reinforcement learning platform"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "a study on overfitting in deep reinforcement learning"
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "neural predictive belief representations"):
  with members:
    - "integrating algorithmic planning and deep learning for partially observable navigation"
    - "unsupervised predictive memory in a goal directed agent"
    - "learning awareness models"
    - "representation learning with contrastive predictive coding"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "learning to act by predicting the future"
    - "deep residual learning for image recognition"
    - "bayesian 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.LG(Machine Learning)