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: "reinforcement learning via recurrent convolutional neural networks"
  Abstract: Deep Reinforcement Learning has enabled the learning of policies for complex tasks in partially observable environments, without explicitly learning the underlying model of the tasks. While such model-free methods achieve considerable performance, they often ignore the structure of task. We present a natural representation of to Reinforcement Learning (RL) problems using Recurrent Convolutional Neural Networks (RCNNs), to better exploit this inherent structure. We define 3 such RCNNs, whose forward passes execute an efficient Value Iteration, propagate beliefs of state in partially observable environments, and choose optimal actions respectively. Backpropagating gradients through these RCNNs allows the system to explicitly learn the Transition Model and Reward Function associated with the underlying MDP, serving as an elegant alternative to classical model-based RL. We evaluate the proposed algorithms in simulation, considering a robot planning problem. We demonstrate the capability of our framework to reduce the cost of replanning, learn accurate MDP models, and finally re-plan with learnt models to achieve near-optimal policies.

Connected through hyperedge HE-1 (induced by source paper "learning navigation costs from demonstration in partially observable environments"):
  with members:
    - "differentiable algorithm networks for composable robot learning"
    - "path integral networks end to end differentiable optimal control"
    - "memory augmented control networks"
    - "qmdp net deep learning for planning under partial observability"
    - "cognitive mapping and planning for visual navigation"
    - "watch this scalable cost function learning for path planning in urban environments"
    - "value iteration networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-2 (induced by source paper "dual sequential monte carlo tunneling filtering and planning in continuous pomdps"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "integrating algorithmic planning and deep learning for partially observable navigation"
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "spectral normalization for generative adversarial networks"
    - "particle filter networks with application to visual localization"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "deep variational reinforcement learning for pomdps"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-3 (induced by source paper "partially observable planning and learning for systems with non uniform dynamics"):
  with members:
    - "deep reinforcement learning a brief survey"
    - "belief state planning for autonomously navigating urban intersections"
    - "qmdp net deep learning for planning under partial observability"
    - "value iteration networks"
    - "backprop kf learning discriminative deterministic state estimators"
    - "learning to navigate in complex environments"
    - "deep recurrent q learning for partially observable mdps"
    - "bayesian reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "particle filter networks with application to visual localization"):
  with members:
    - "differentiable particle filters end to end learning with algorithmic priors"
    - "learning to search with mctsnets"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "filtering variational objectives"
    - "value prediction network"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "optnet differentiable optimization as a layer in neural networks"

Connected through hyperedge HE-5 (induced by source paper "integrating algorithmic planning and deep learning for partially observable navigation"):
  with members:
    - "learning to search with mctsnets"
    - "particle filter networks with application to visual localization"
    - "treeqn and atreec differentiable tree structured models for deep reinforcement learning"
    - "value prediction network"
    - "optnet differentiable optimization as a layer in neural networks"
    - "task based end to end model learning in stochastic optimization"
    - "path integral networks end to end differentiable optimal control"
    - "qmdp net deep learning for planning under partial observability"

Connected through hyperedge HE-6 (induced by source paper "differentiable particle filters end to end learning with algorithmic priors"):
  with members:
    - "path integral networks end to end differentiable optimal control"
    - "cognitive mapping and planning for visual navigation"
    - "the predictron end to end learning and planning"
    - "value iteration networks"
    - "backprop kf learning discriminative deterministic state estimators"
    - "deep residual learning for image recognition"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "qmdp net deep learning for planning under partial observability"):
  with members:
    - "path integral networks end to end differentiable optimal control"
    - "despot online pomdp planning with regularization"
    - "cognitive mapping and planning for visual navigation"
    - "value iteration networks"
    - "backprop kf learning discriminative deterministic state estimators"
    - "learning to navigate in complex environments"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-8 (induced by source paper "pedestrian prediction by planning using deep neural networks"):
  with members:
    - "the cityscapes dataset for semantic urban scene understanding"
    - "value iteration networks"
    - "going deeper with convolutions"
    - "fully convolutional networks for semantic segmentation"

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)