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: "integrating algorithmic planning and deep learning for partially observable navigation"
  Abstract: We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network. This representation allows for integrating algorithmic planning and deep learning in a principled manner, and thus combine the benefits of model-free and model-based methods. We apply the proposed approach to a challenging partially observable robot navigation task. The robot must navigate to a goal in a previously unseen 3-D environment without knowing its initial location, and instead relying on a 2-D floor map and visual observations from an onboard camera. We introduce the Navigation Networks (NavNets) that encode state estimation, planning and acting in a single, end-to-end trainable recurrent neural network. In preliminary simulation experiments we successfully trained navigation networks to solve the challenging partially observable navigation task.

Connected through hyperedge HE-1 (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"
    - "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"
    - "online algorithms for pomdps with continuous state action and observation spaces"

Connected through hyperedge HE-2 (induced by source paper "neural predictive belief representations"):
  with members:
    - "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"
    - "recurrent reinforcement learning a hybrid approach"
    - "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.RO(Robotics)