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: "representing robot task plans as robust logical dynamical systems"
  Abstract: It is difficult to create robust, reusable, and reactive behaviors for robots that can be easily extended and combined. Frameworks such as Behavior Trees are flexible but difficult to characterize, especially when designing reactions and recovery behaviors to consistently converge to a desired goal condition. We propose a framework which we call Robust Logical-Dynamical Systems (RLDS), which combines the advantages of task representations like behavior trees with theoretical guarantees on performance. RLDS can also be constructed automatically from simple sequential task plans and will still achieve robust, reactive behavior in dynamic real-world environments. In this work, we describe both our proposed framework and a case study on a simple household manipulation task, with examples for how specific pieces can be implemented to achieve robust behavior. Finally, we show how in the context of these manipulation tasks, a combination of an RLDS with planning can achieve better results under adversarial conditions.

Connected through hyperedge HE-1 (induced by source paper "transferable task execution from pixels through deep planning domain learning"):
  with members:
    - "prospection interpretable plans from language by predicting the future"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "visual robot task planning"
    - "learning generalized reactive policies using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "human grasp classification for reactive human to robot handovers"):
  with members:
    - "transferable task execution from pixels through deep planning domain learning"
    - "3d hand shape and pose estimation from a single rgb image"
    - "freihand a dataset for markerless capture of hand pose and shape from single rgb images"
    - "learning joint reconstruction of hands and manipulated objects"
    - "6 dof grasping for target driven object manipulation in clutter"
    - "honnotate a method for 3d annotation of hand and objects poses"
    - "online replanning in belief space for partially observable task and motion problems"
    - "dexpilot vision based teleoperation of dexterous robotic hand arm system"

Connected through hyperedge HE-3 (induced by source paper "motion reasoning for goal based imitation learning"):
  with members:
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "dexpilot vision based teleoperation of dexterous robotic hand arm system"
    - "scaling egocentric vision the epic kitchens dataset"
    - "explicability legibility predictability transparency privacy security the emerging landscape of interpretable agent behavior"
    - "zero shot visual imitation"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "beyond imitation zero shot task transfer on robots by learning concepts as cognitive programs"
    - "rmpflow a computational graph for automatic motion policy generation"

Connected through hyperedge HE-4 (induced by source paper "riemannian motion policy fusion through learnable lyapunov function reshaping"):
  with members:
    - "stable concurrent controller composition for multi objective robotic tasks"
    - "multi objective policy generation for multi robot systems using riemannian motion policies"
    - "learning latent space dynamics for tactile servoing"
    - "riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "convergence of value aggregation for imitation learning"
    - "fast policy learning through imitation and reinforcement"
    - "approximately optimal continuous time motion planning and control via probabilistic inference"

Connected through hyperedge HE-5 (induced by source paper "online replanning in belief space for partially observable task and motion problems"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "bounded policy synthesis for pomdps with safe reachability objectives"
    - "sampling based methods for factored task and motion planning"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "an anytime algorithm for task and motion mdps"
    - "ffrob leveraging symbolic planning for efficient task and motion planning"
    - "posecnn a convolutional neural network for 6d object pose estimation in cluttered scenes"
    - "pddl2 1 an extension to pddl for expressing temporal planning domains"

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)