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: "memory based control with recurrent neural networks"
  Abstract: Partially observed control problems are a challenging aspect of reinforcement learning. We extend two related, model-free algorithms for continuous control -- deterministic policy gradient and stochastic value gradient -- to solve partially observed domains using recurrent neural networks trained with backpropagation through time. #R##N#We demonstrate that this approach, coupled with long-short term memory is able to solve a variety of physical control problems exhibiting an assortment of memory requirements. These include the short-term integration of information from noisy sensors and the identification of system parameters, as well as long-term memory problems that require preserving information over many time steps. We also demonstrate success on a combined exploration and memory problem in the form of a simplified version of the well-known Morris water maze task. Finally, we show that our approach can deal with high-dimensional observations by learning directly from pixels. #R##N#We find that recurrent deterministic and stochastic policies are able to learn similarly good solutions to these tasks, including the water maze where the agent must learn effective search strategies.

Connected through hyperedge HE-1 (induced by source paper "dynamic energy dispatch in isolated microgrids based on deep reinforcement learning"):
  with members:
    - "deep reinforcement learning for autonomous internet of things model applications and challenges"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "interference management for cellular connected uavs a deep reinforcement learning approach"
    - "vehicular edge computing via deep reinforcement learning"
    - "leveraging machine learning and big data for smart buildings a comprehensive survey"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "toward intelligent vehicular networks a machine learning framework"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "cooperative deep reinforcement learning for multiple group nb iot networks optimization"

Connected through hyperedge HE-3 (induced by source paper "offworld gym open access physical robotics environment for real world reinforcement learning benchmark and research"):
  with members:
    - "replab a reproducible low cost arm benchmark platform for robotic learning"
    - "learning latent state representation for speeding up exploration"
    - "go explore a new approach for hard exploration problems"
    - "pyrep bringing v rep to deep robot learning"
    - "self supervised exploration via disagreement"
    - "provably efficient imitation learning from observation alone"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "challenges of real world reinforcement learning"

Connected through hyperedge HE-4 (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"
    - "one big net for everything"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "efficient off policy meta reinforcement learning via probabilistic context variables"):
  with members:
    - "some considerations on learning to explore via meta reinforcement learning"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "meta gradient reinforcement learning"
    - "bayesian model agnostic meta learning"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "tadam task dependent adaptive metric for improved few shot learning"

Connected through hyperedge HE-6 (induced by source paper "meta q learning"):
  with members:
    - "a baseline for few shot image classification"
    - "p3o policy on policy off policy optimization"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "learning internal representations"
    - "on first order meta learning algorithms"
    - "off policy deep reinforcement learning without exploration"
    - "addressing function approximation error in actor critic methods"
    - "evolved policy gradients"

Connected through hyperedge HE-7 (induced by source paper "emergent coordination through competition"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "deepmind control suite"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"
    - "counterfactual multi agent policy gradients"
    - "stabilising experience replay for deep multi agent reinforcement learning"
    - "starcraft ii a new challenge for reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "variational recurrent models for solving partially observable control tasks"):
  with members:
    - "self organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networks"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "model based reinforcement learning for atari"
    - "learning latent dynamics for planning from pixels"
    - "a novel predictive coding inspired variational rnn model for online prediction and recognition"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep variational reinforcement learning for pomdps"

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)