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: "ffrob leveraging symbolic planning for efficient task and motion planning"
  Abstract: Mobile manipulation problems involving many objects are challenging to solve due to the high dimensionality and multi-modality of their hybrid configuration spaces. Planners that perform a purely g...

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"
    - "deep sequential models for sampling based planning"
    - "follownet robot navigation by following natural language directions with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "task motion planning for safe and efficient urban driving"):
  with members:
    - "interpretable end to end urban autonomous driving with latent deep reinforcement learning"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "learning from interventions using hierarchical policies for safe learning"
    - "augmenting knowledge through statistical goal oriented human robot dialog"
    - "learning to guide task and motion planning using score space representation"
    - "carla an open urban driving simulator"
    - "combining neural networks and tree search for task and motion planning in challenging environments"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"

Connected through hyperedge HE-3 (induced by source paper "socially intelligent task and motion planning for human robot interaction"):
  with members:
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"
    - "probabilistic motion planning under temporal tasks and soft constraints"

Connected through hyperedge HE-4 (induced by source paper "hrl4in hierarchical reinforcement learning for interactive navigation with mobile manipulators"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "dot to dot explainable hierarchical reinforcement learning for robotic manipulation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "learning navigation behaviors end to end with autorl"
    - "gibson env real world perception for embodied agents"
    - "learning multi level hierarchies with hindsight"

Connected through hyperedge HE-5 (induced by source paper "sample complexity of probabilistic roadmaps via e nets"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "toward asymptotically optimal inspection planning via efficient near optimal graph search"
    - "robot motion planning in learned latent spaces"
    - "fast anytime motion planning for prehensile manipulation in clutter"
    - "learning sampling distributions for robot motion planning"
    - "the critical radius in sampling based motion planning"
    - "data driven planning via imitation learning"
    - "new perspective on sampling based motion planning via random geometric graphs"

Connected through hyperedge HE-6 (induced by source paper "anytime multi arm task and motion planning for pick and place of individual objects via handoffs"):
  with members:
    - "drrt scalable and informed asymptotically optimal multi robot motion planning"
    - "towards robust product packing with a minimalistic end effector"
    - "admissible abstractions for near optimal task and motion planning"
    - "fast high quality dual arm rearrangement in synchronous monotone tabletop setups"
    - "scalable asymptotically optimal multi robot motion planning"
    - "analysis and observations from the first amazon picking challenge"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"
    - "on the power of manifold samples in exploring configuration spaces and the dimensionality of narrow passages"

Connected through hyperedge HE-7 (induced by source paper "object rearrangement with nested nonprehensile manipulation actions"):
  with members:
    - "towards robust product packing with a minimalistic end effector"
    - "inferring 3d shapes of unknown rigid objects in clutter through inverse physics reasoning"
    - "sample efficient learning of nonprehensile manipulation policies via physics based informed state distributions"
    - "sampling based methods for factored task and motion planning"
    - "automated motion planning for robotic assembly of discrete architectural structures"
    - "rearrangement with nonprehensile manipulation using deep reinforcement learning"
    - "efficient high quality stack rearrangement"
    - "finding a needle in an exponential haystack discrete rrt for exploration of implicit roadmaps in multi robot motion planning"

Connected through hyperedge HE-8 (induced by source paper "multi object rearrangement with monte carlo tree search a case study on planar nonprehensile sorting"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "learning manipulation states and actions for efficient non prehensile rearrangement planning"
    - "monte carlo tree search for efficient visually guided rearrangement planning"
    - "sample efficient learning of nonprehensile manipulation policies via physics based informed state distributions"
    - "planning with a receding horizon for manipulation in clutter using a learned value function"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "high quality tabletop rearrangement with overhand grasps hardness results and fast methods"

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)