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 sequential models for sampling based planning"
  Abstract: We demonstrate how a sequence model and a sampling-based planner can influence each other to produce efficient plans and how such a model can automatically learn to take advantage of observations of the environment. Sampling-based planners such as RRT generally know nothing of their environments even if they have traversed similar spaces many times. A sequence model, such as an HMM or LSTM, guides the search for good paths. The resulting model, called DeRRT*, observes the state of the planner and the local environment to bias the next move and next planner state. The neural-network-based models avoid manual feature engineering by co-training a convolutional network which processes map features and observations from sensors. We incorporate this sequence model in a manner that combines its likelihood with the existing bias for searching large unexplored Voronoi regions. This leads to more efficient trajectories with fewer rejected samples even in difficult domains such as when escaping bug traps. This model can also be used for dimensionality reduction in multi-agent environments with dynamic obstacles. Instead of planning in a high-dimensional space that includes the configurations of the other agents, we plan in a low-dimensional subspace relying on the sequence model to bias samples using the observed behavior of the other agents. The techniques presented here are general, include both graphical models and deep learning approaches, and can be adapted to a range of planners.

Connected through hyperedge HE-1 (induced by source paper "deep compositional robotic planners that follow natural language commands"):
  with members:
    - "from semantics to execution integrating action planning with reinforcement learning for robotic causal problem solving"
    - "from language to goals inverse reinforcement learning for vision based instruction following"
    - "from semantics to execution integrating action planning with reinforcement learning for robotic tool use"
    - "prospection interpretable plans from language by predicting the future"
    - "learning to plan via neural exploration exploitation trees"
    - "mapping instructions to actions in 3d environments with visual goal prediction"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"
    - "gated path planning networks"

Connected through hyperedge HE-2 (induced by source paper "harnessing reinforcement learning for neural motion planning"):
  with members:
    - "robot motion planning in learned latent spaces"
    - "auto conditioned recurrent mixture density networks for complex trajectory generation"
    - "universal planning networks"
    - "hierarchical reinforcement learning with hindsight"
    - "deep learning using rectified linear units relu"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "reinforced imitation sample efficient deep reinforcement learning for map less navigation by leveraging prior demonstrations"
    - "motion planning networks"

Connected through hyperedge HE-3 (induced by source paper "learning to plan via neural exploration exploitation trees"):
  with members:
    - "learning to search with mctsnets"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "gated path planning networks"
    - "learning implicit sampling distributions for motion planning"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "learning sampling distributions for robot motion planning"
    - "qmdp net deep learning for planning under partial observability"

Connected through hyperedge HE-4 (induced by source paper "lego leveraging experience in roadmap generation for sampling based planning"):
  with members:
    - "using local experiences for global motion planning"
    - "robot motion planning in learned latent spaces"
    - "deeply informed neural sampling for robot motion planning"
    - "learning implicit sampling distributions for motion planning"
    - "approximate inference based motion planning by learning and exploiting low dimensional latent variable models"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "generating long term trajectories using deep hierarchical networks"
    - "learning sampling distributions for robot motion planning"

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)